nn2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-08-24T00:53:43.819563Z",
     "start_time": "2024-08-24T00:53:43.029417Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:53:43.969766Z",
     "start_time": "2024-08-24T00:53:43.898712Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df = pd.read_csv(\"DNN_data.csv\", parse_dates = [\"time\"], index_col = \"time\")\n",
    "symbol = df.columns[0]\n"
   ],
   "id": "1cf3905c795394ba",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:53:44.823691Z",
     "start_time": "2024-08-24T00:53:44.815858Z"
    }
   },
   "cell_type": "code",
   "source": "df.info()",
   "id": "c67a3bee987cf16d",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 55785 entries, 2017-01-02 23:20:00 to 2019-12-31 00:20:00\n",
      "Data columns (total 1 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   EUR_USD  55785 non-null  float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 871.6 KB\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:53:47.032801Z",
     "start_time": "2024-08-24T00:53:47.025283Z"
    }
   },
   "cell_type": "code",
   "source": [
    "window = 50\n",
    "long_sma = 150\n",
    "momentum_window = 3"
   ],
   "id": "e4448225c06e3d10",
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:53:48.489664Z",
     "start_time": "2024-08-24T00:53:48.219835Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df.plot(figsize=(12, 8))\n",
    "plt.show()"
   ],
   "id": "39d062abca236a62",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:53:51.411939Z",
     "start_time": "2024-08-24T00:53:51.404927Z"
    }
   },
   "cell_type": "code",
   "source": "df[\"returns\"] = np.log(df[symbol] / df[symbol].shift())",
   "id": "f0ed0254fb0b57cd",
   "outputs": [],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:53:52.980969Z",
     "start_time": "2024-08-24T00:53:52.969806Z"
    }
   },
   "cell_type": "code",
   "source": "df",
   "id": "fab70cd55073e29c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                     EUR_USD   returns\n",
       "time                                  \n",
       "2017-01-02 23:20:00  1.04574       NaN\n",
       "2017-01-02 23:40:00  1.04608  0.000325\n",
       "2017-01-03 00:00:00  1.04575 -0.000316\n",
       "2017-01-03 00:20:00  1.04672  0.000927\n",
       "2017-01-03 00:40:00  1.04696  0.000229\n",
       "...                      ...       ...\n",
       "2019-12-30 23:00:00  1.12012  0.000080\n",
       "2019-12-30 23:20:00  1.12030  0.000161\n",
       "2019-12-30 23:40:00  1.12018 -0.000107\n",
       "2019-12-31 00:00:00  1.12013 -0.000045\n",
       "2019-12-31 00:20:00  1.12030  0.000152\n",
       "\n",
       "[55785 rows x 2 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EUR_USD</th>\n",
       "      <th>returns</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-02 23:20:00</th>\n",
       "      <td>1.04574</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02 23:40:00</th>\n",
       "      <td>1.04608</td>\n",
       "      <td>0.000325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03 00:00:00</th>\n",
       "      <td>1.04575</td>\n",
       "      <td>-0.000316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03 00:20:00</th>\n",
       "      <td>1.04672</td>\n",
       "      <td>0.000927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03 00:40:00</th>\n",
       "      <td>1.04696</td>\n",
       "      <td>0.000229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30 23:00:00</th>\n",
       "      <td>1.12012</td>\n",
       "      <td>0.000080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30 23:20:00</th>\n",
       "      <td>1.12030</td>\n",
       "      <td>0.000161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30 23:40:00</th>\n",
       "      <td>1.12018</td>\n",
       "      <td>-0.000107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 00:00:00</th>\n",
       "      <td>1.12013</td>\n",
       "      <td>-0.000045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 00:20:00</th>\n",
       "      <td>1.12030</td>\n",
       "      <td>0.000152</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55785 rows × 2 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:53:57.429991Z",
     "start_time": "2024-08-24T00:53:57.399877Z"
    }
   },
   "cell_type": "code",
   "source": [
    "data = df.copy()\n",
    "data['dir'] = np.where(data['returns'] > 0, 1,0)\n",
    "data['sma'] = data[symbol].rolling(window).mean() - data[symbol].rolling(long_sma).mean()\n",
    "data['boll'] = (data[symbol] - data[symbol].rolling(window).mean()) / data[symbol].rolling(window).std()\n",
    "data['min'] = data[symbol].rolling(window).min() / data[symbol] - 1\n",
    "data['max'] = data[symbol].rolling(window).max() / data[symbol] - 1\n",
    "data['mom'] = data['returns'].rolling(momentum_window).mean()\n",
    "data['vol'] = data['returns'].rolling(window).std()\n",
    "data.dropna(inplace=True)\n",
    "data"
   ],
   "id": "92870124c545142c",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                     EUR_USD   returns  dir       sma      boll       min  \\\n",
       "time                                                                        \n",
       "2017-01-05 01:00:00  1.05406  0.001728    1  0.002748  2.777610 -0.010948   \n",
       "2017-01-05 01:20:00  1.05226 -0.001709    0  0.002899  2.000277 -0.009047   \n",
       "2017-01-05 01:40:00  1.05138 -0.000837    0  0.003024  1.604398 -0.008218   \n",
       "2017-01-05 02:00:00  1.05196  0.000552    1  0.003139  1.718860 -0.008765   \n",
       "2017-01-05 02:20:00  1.05230  0.000323    1  0.003266  1.742715 -0.009085   \n",
       "...                      ...       ...  ...       ...       ...       ...   \n",
       "2019-12-30 23:00:00  1.12012  0.000080    1  0.003163  0.116062 -0.001357   \n",
       "2019-12-30 23:20:00  1.12030  0.000161    1  0.003116  0.335572 -0.001517   \n",
       "2019-12-30 23:40:00  1.12018 -0.000107    0  0.003058  0.155421 -0.001410   \n",
       "2019-12-31 00:00:00  1.12013 -0.000045    0  0.002986  0.090286 -0.001366   \n",
       "2019-12-31 00:20:00  1.12030  0.000152    1  0.002913  0.327692 -0.001517   \n",
       "\n",
       "                          max           mom       vol  \n",
       "time                                                   \n",
       "2017-01-05 01:00:00  0.000000  1.384831e-03  0.000803  \n",
       "2017-01-05 01:20:00  0.001711 -1.013537e-04  0.000841  \n",
       "2017-01-05 01:40:00  0.002549 -2.725461e-04  0.000848  \n",
       "2017-01-05 02:00:00  0.001996 -6.647612e-04  0.000847  \n",
       "2017-01-05 02:20:00  0.001673  1.267090e-05  0.000847  \n",
       "...                       ...           ...       ...  \n",
       "2019-12-30 23:00:00  0.001446  5.952275e-05  0.000407  \n",
       "2019-12-30 23:20:00  0.001285 -2.975261e-05  0.000396  \n",
       "2019-12-30 23:40:00  0.001393  4.463867e-05  0.000395  \n",
       "2019-12-31 00:00:00  0.001437  2.975858e-06  0.000385  \n",
       "2019-12-31 00:20:00  0.001285 -2.684304e-17  0.000385  \n",
       "\n",
       "[55636 rows x 9 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EUR_USD</th>\n",
       "      <th>returns</th>\n",
       "      <th>dir</th>\n",
       "      <th>sma</th>\n",
       "      <th>boll</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mom</th>\n",
       "      <th>vol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-05 01:00:00</th>\n",
       "      <td>1.05406</td>\n",
       "      <td>0.001728</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002748</td>\n",
       "      <td>2.777610</td>\n",
       "      <td>-0.010948</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.384831e-03</td>\n",
       "      <td>0.000803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05 01:20:00</th>\n",
       "      <td>1.05226</td>\n",
       "      <td>-0.001709</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002899</td>\n",
       "      <td>2.000277</td>\n",
       "      <td>-0.009047</td>\n",
       "      <td>0.001711</td>\n",
       "      <td>-1.013537e-04</td>\n",
       "      <td>0.000841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05 01:40:00</th>\n",
       "      <td>1.05138</td>\n",
       "      <td>-0.000837</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003024</td>\n",
       "      <td>1.604398</td>\n",
       "      <td>-0.008218</td>\n",
       "      <td>0.002549</td>\n",
       "      <td>-2.725461e-04</td>\n",
       "      <td>0.000848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05 02:00:00</th>\n",
       "      <td>1.05196</td>\n",
       "      <td>0.000552</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003139</td>\n",
       "      <td>1.718860</td>\n",
       "      <td>-0.008765</td>\n",
       "      <td>0.001996</td>\n",
       "      <td>-6.647612e-04</td>\n",
       "      <td>0.000847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05 02:20:00</th>\n",
       "      <td>1.05230</td>\n",
       "      <td>0.000323</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003266</td>\n",
       "      <td>1.742715</td>\n",
       "      <td>-0.009085</td>\n",
       "      <td>0.001673</td>\n",
       "      <td>1.267090e-05</td>\n",
       "      <td>0.000847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30 23:00:00</th>\n",
       "      <td>1.12012</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003163</td>\n",
       "      <td>0.116062</td>\n",
       "      <td>-0.001357</td>\n",
       "      <td>0.001446</td>\n",
       "      <td>5.952275e-05</td>\n",
       "      <td>0.000407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30 23:20:00</th>\n",
       "      <td>1.12030</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003116</td>\n",
       "      <td>0.335572</td>\n",
       "      <td>-0.001517</td>\n",
       "      <td>0.001285</td>\n",
       "      <td>-2.975261e-05</td>\n",
       "      <td>0.000396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30 23:40:00</th>\n",
       "      <td>1.12018</td>\n",
       "      <td>-0.000107</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003058</td>\n",
       "      <td>0.155421</td>\n",
       "      <td>-0.001410</td>\n",
       "      <td>0.001393</td>\n",
       "      <td>4.463867e-05</td>\n",
       "      <td>0.000395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 00:00:00</th>\n",
       "      <td>1.12013</td>\n",
       "      <td>-0.000045</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002986</td>\n",
       "      <td>0.090286</td>\n",
       "      <td>-0.001366</td>\n",
       "      <td>0.001437</td>\n",
       "      <td>2.975858e-06</td>\n",
       "      <td>0.000385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 00:20:00</th>\n",
       "      <td>1.12030</td>\n",
       "      <td>0.000152</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002913</td>\n",
       "      <td>0.327692</td>\n",
       "      <td>-0.001517</td>\n",
       "      <td>0.001285</td>\n",
       "      <td>-2.684304e-17</td>\n",
       "      <td>0.000385</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55636 rows × 9 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:53:59.959174Z",
     "start_time": "2024-08-24T00:53:59.921847Z"
    }
   },
   "cell_type": "code",
   "source": "data.describe()",
   "id": "39c790f7641c8a93",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            EUR_USD       returns           dir           sma          boll  \\\n",
       "count  55636.000000  55636.000000  55636.000000  55636.000000  55636.000000   \n",
       "mean       1.143866      0.000001      0.492181      0.000068      0.001570   \n",
       "std        0.045713      0.000495      0.499943      0.002585      1.437175   \n",
       "min        1.046380     -0.008392      0.000000     -0.012869     -6.264949   \n",
       "25%        1.114200     -0.000214      0.000000     -0.001613     -1.044366   \n",
       "50%        1.137630      0.000000      0.000000     -0.000010      0.016489   \n",
       "75%        1.175020      0.000216      1.000000      0.001662      1.062898   \n",
       "max        1.254940      0.015430      1.000000      0.009903      6.321360   \n",
       "\n",
       "                min           max           mom           vol  \n",
       "count  55636.000000  55636.000000  5.563600e+04  55636.000000  \n",
       "mean      -0.002251      0.002177  1.137574e-06      0.000462  \n",
       "std        0.002205      0.002097  2.823596e-04      0.000182  \n",
       "min       -0.019673      0.000000 -3.643085e-03      0.000082  \n",
       "25%       -0.003194      0.000642 -1.225118e-04      0.000338  \n",
       "50%       -0.001570      0.001584  2.904194e-17      0.000435  \n",
       "75%       -0.000665      0.003129  1.226404e-04      0.000550  \n",
       "max        0.000000      0.023081  5.771696e-03      0.002356  "
      ],
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EUR_USD</th>\n",
       "      <th>returns</th>\n",
       "      <th>dir</th>\n",
       "      <th>sma</th>\n",
       "      <th>boll</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mom</th>\n",
       "      <th>vol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>55636.000000</td>\n",
       "      <td>55636.000000</td>\n",
       "      <td>55636.000000</td>\n",
       "      <td>55636.000000</td>\n",
       "      <td>55636.000000</td>\n",
       "      <td>55636.000000</td>\n",
       "      <td>55636.000000</td>\n",
       "      <td>5.563600e+04</td>\n",
       "      <td>55636.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.143866</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.492181</td>\n",
       "      <td>0.000068</td>\n",
       "      <td>0.001570</td>\n",
       "      <td>-0.002251</td>\n",
       "      <td>0.002177</td>\n",
       "      <td>1.137574e-06</td>\n",
       "      <td>0.000462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.045713</td>\n",
       "      <td>0.000495</td>\n",
       "      <td>0.499943</td>\n",
       "      <td>0.002585</td>\n",
       "      <td>1.437175</td>\n",
       "      <td>0.002205</td>\n",
       "      <td>0.002097</td>\n",
       "      <td>2.823596e-04</td>\n",
       "      <td>0.000182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.046380</td>\n",
       "      <td>-0.008392</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.012869</td>\n",
       "      <td>-6.264949</td>\n",
       "      <td>-0.019673</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.643085e-03</td>\n",
       "      <td>0.000082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.114200</td>\n",
       "      <td>-0.000214</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.001613</td>\n",
       "      <td>-1.044366</td>\n",
       "      <td>-0.003194</td>\n",
       "      <td>0.000642</td>\n",
       "      <td>-1.225118e-04</td>\n",
       "      <td>0.000338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.137630</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000010</td>\n",
       "      <td>0.016489</td>\n",
       "      <td>-0.001570</td>\n",
       "      <td>0.001584</td>\n",
       "      <td>2.904194e-17</td>\n",
       "      <td>0.000435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.175020</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001662</td>\n",
       "      <td>1.062898</td>\n",
       "      <td>-0.000665</td>\n",
       "      <td>0.003129</td>\n",
       "      <td>1.226404e-04</td>\n",
       "      <td>0.000550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.254940</td>\n",
       "      <td>0.015430</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.009903</td>\n",
       "      <td>6.321360</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.023081</td>\n",
       "      <td>5.771696e-03</td>\n",
       "      <td>0.002356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:54:02.263357Z",
     "start_time": "2024-08-24T00:54:02.259728Z"
    }
   },
   "cell_type": "code",
   "source": "lags = 5",
   "id": "d72a97de156ce139",
   "outputs": [],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:54:03.007617Z",
     "start_time": "2024-08-24T00:54:02.999570Z"
    }
   },
   "cell_type": "code",
   "source": [
    "cols = []\n",
    "features = ['dir', 'sma', 'boll', 'min', 'max', 'mom', 'vol']"
   ],
   "id": "e20a948e02499228",
   "outputs": [],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:54:05.430137Z",
     "start_time": "2024-08-24T00:54:05.349841Z"
    }
   },
   "cell_type": "code",
   "source": [
    "for f in features:\n",
    "    for lag in range(1, lags + 1):\n",
    "        col = \"{}_lag_{}\".format(f, lag)\n",
    "        data[col] = data[f].shift(lag)\n",
    "        cols.append(col)\n",
    "data.dropna(inplace=True)\n",
    "data"
   ],
   "id": "bed08d1e64525078",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                     EUR_USD   returns  dir       sma      boll       min  \\\n",
       "time                                                                        \n",
       "2017-01-05 02:40:00  1.05124 -0.001008    0  0.003386  1.322766 -0.008086   \n",
       "2017-01-05 03:00:00  1.05150  0.000247    1  0.003512  1.355157 -0.008331   \n",
       "2017-01-05 03:20:00  1.05230  0.000761    1  0.003652  1.557089 -0.009085   \n",
       "2017-01-05 03:40:00  1.05192 -0.000361    0  0.003799  1.375429 -0.008727   \n",
       "2017-01-05 04:00:00  1.05204  0.000114    1  0.003922  1.357637 -0.008840   \n",
       "...                      ...       ...  ...       ...       ...       ...   \n",
       "2019-12-30 23:00:00  1.12012  0.000080    1  0.003163  0.116062 -0.001357   \n",
       "2019-12-30 23:20:00  1.12030  0.000161    1  0.003116  0.335572 -0.001517   \n",
       "2019-12-30 23:40:00  1.12018 -0.000107    0  0.003058  0.155421 -0.001410   \n",
       "2019-12-31 00:00:00  1.12013 -0.000045    0  0.002986  0.090286 -0.001366   \n",
       "2019-12-31 00:20:00  1.12030  0.000152    1  0.002913  0.327692 -0.001517   \n",
       "\n",
       "                          max           mom       vol  dir_lag_1  ...  \\\n",
       "time                                                              ...   \n",
       "2017-01-05 02:40:00  0.002683 -4.438906e-05  0.000860        1.0  ...   \n",
       "2017-01-05 03:00:00  0.002435 -1.457915e-04  0.000859        0.0  ...   \n",
       "2017-01-05 03:20:00  0.001673 -1.362481e-17  0.000863        1.0  ...   \n",
       "2017-01-05 03:40:00  0.002034  2.155487e-04  0.000862        1.0  ...   \n",
       "2017-01-05 04:00:00  0.001920  1.711401e-04  0.000852        0.0  ...   \n",
       "...                       ...           ...       ...        ...  ...   \n",
       "2019-12-30 23:00:00  0.001446  5.952275e-05  0.000407        0.0  ...   \n",
       "2019-12-30 23:20:00  0.001285 -2.975261e-05  0.000396        1.0  ...   \n",
       "2019-12-30 23:40:00  0.001393  4.463867e-05  0.000395        1.0  ...   \n",
       "2019-12-31 00:00:00  0.001437  2.975858e-06  0.000385        0.0  ...   \n",
       "2019-12-31 00:20:00  0.001285 -2.684304e-17  0.000385        0.0  ...   \n",
       "\n",
       "                        mom_lag_1     mom_lag_2  mom_lag_3  mom_lag_4  \\\n",
       "time                                                                    \n",
       "2017-01-05 02:40:00  1.267090e-05 -6.647612e-04  -0.000273  -0.000101   \n",
       "2017-01-05 03:00:00 -4.438906e-05  1.267090e-05  -0.000665  -0.000273   \n",
       "2017-01-05 03:20:00 -1.457915e-04 -4.438906e-05   0.000013  -0.000665   \n",
       "2017-01-05 03:40:00 -1.362481e-17 -1.457915e-04  -0.000044   0.000013   \n",
       "2017-01-05 04:00:00  2.155487e-04 -1.362481e-17  -0.000146  -0.000044   \n",
       "...                           ...           ...        ...        ...   \n",
       "2019-12-30 23:00:00 -6.844352e-05  6.545924e-05   0.000021   0.000104   \n",
       "2019-12-30 23:20:00  5.952275e-05 -6.844352e-05   0.000065   0.000021   \n",
       "2019-12-30 23:40:00 -2.975261e-05  5.952275e-05  -0.000068   0.000065   \n",
       "2019-12-31 00:00:00  4.463867e-05 -2.975261e-05   0.000060  -0.000068   \n",
       "2019-12-31 00:20:00  2.975858e-06  4.463867e-05  -0.000030   0.000060   \n",
       "\n",
       "                     mom_lag_5  vol_lag_1  vol_lag_2  vol_lag_3  vol_lag_4  \\\n",
       "time                                                                         \n",
       "2017-01-05 02:40:00   0.001385   0.000847   0.000847   0.000848   0.000841   \n",
       "2017-01-05 03:00:00  -0.000101   0.000860   0.000847   0.000847   0.000848   \n",
       "2017-01-05 03:20:00  -0.000273   0.000859   0.000860   0.000847   0.000847   \n",
       "2017-01-05 03:40:00  -0.000665   0.000863   0.000859   0.000860   0.000847   \n",
       "2017-01-05 04:00:00   0.000013   0.000862   0.000863   0.000859   0.000860   \n",
       "...                        ...        ...        ...        ...        ...   \n",
       "2019-12-30 23:00:00   0.000098   0.000408   0.000406   0.000402   0.000401   \n",
       "2019-12-30 23:20:00   0.000104   0.000407   0.000408   0.000406   0.000402   \n",
       "2019-12-30 23:40:00   0.000021   0.000396   0.000407   0.000408   0.000406   \n",
       "2019-12-31 00:00:00   0.000065   0.000395   0.000396   0.000407   0.000408   \n",
       "2019-12-31 00:20:00  -0.000068   0.000385   0.000395   0.000396   0.000407   \n",
       "\n",
       "                     vol_lag_5  \n",
       "time                            \n",
       "2017-01-05 02:40:00   0.000803  \n",
       "2017-01-05 03:00:00   0.000841  \n",
       "2017-01-05 03:20:00   0.000848  \n",
       "2017-01-05 03:40:00   0.000847  \n",
       "2017-01-05 04:00:00   0.000847  \n",
       "...                        ...  \n",
       "2019-12-30 23:00:00   0.000402  \n",
       "2019-12-30 23:20:00   0.000401  \n",
       "2019-12-30 23:40:00   0.000402  \n",
       "2019-12-31 00:00:00   0.000406  \n",
       "2019-12-31 00:20:00   0.000408  \n",
       "\n",
       "[55631 rows x 44 columns]"
      ],
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EUR_USD</th>\n",
       "      <th>returns</th>\n",
       "      <th>dir</th>\n",
       "      <th>sma</th>\n",
       "      <th>boll</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mom</th>\n",
       "      <th>vol</th>\n",
       "      <th>dir_lag_1</th>\n",
       "      <th>...</th>\n",
       "      <th>mom_lag_1</th>\n",
       "      <th>mom_lag_2</th>\n",
       "      <th>mom_lag_3</th>\n",
       "      <th>mom_lag_4</th>\n",
       "      <th>mom_lag_5</th>\n",
       "      <th>vol_lag_1</th>\n",
       "      <th>vol_lag_2</th>\n",
       "      <th>vol_lag_3</th>\n",
       "      <th>vol_lag_4</th>\n",
       "      <th>vol_lag_5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-05 02:40:00</th>\n",
       "      <td>1.05124</td>\n",
       "      <td>-0.001008</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003386</td>\n",
       "      <td>1.322766</td>\n",
       "      <td>-0.008086</td>\n",
       "      <td>0.002683</td>\n",
       "      <td>-4.438906e-05</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.267090e-05</td>\n",
       "      <td>-6.647612e-04</td>\n",
       "      <td>-0.000273</td>\n",
       "      <td>-0.000101</td>\n",
       "      <td>0.001385</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.000848</td>\n",
       "      <td>0.000841</td>\n",
       "      <td>0.000803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05 03:00:00</th>\n",
       "      <td>1.05150</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003512</td>\n",
       "      <td>1.355157</td>\n",
       "      <td>-0.008331</td>\n",
       "      <td>0.002435</td>\n",
       "      <td>-1.457915e-04</td>\n",
       "      <td>0.000859</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.438906e-05</td>\n",
       "      <td>1.267090e-05</td>\n",
       "      <td>-0.000665</td>\n",
       "      <td>-0.000273</td>\n",
       "      <td>-0.000101</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.000848</td>\n",
       "      <td>0.000841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05 03:20:00</th>\n",
       "      <td>1.05230</td>\n",
       "      <td>0.000761</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003652</td>\n",
       "      <td>1.557089</td>\n",
       "      <td>-0.009085</td>\n",
       "      <td>0.001673</td>\n",
       "      <td>-1.362481e-17</td>\n",
       "      <td>0.000863</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.457915e-04</td>\n",
       "      <td>-4.438906e-05</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>-0.000665</td>\n",
       "      <td>-0.000273</td>\n",
       "      <td>0.000859</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.000848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05 03:40:00</th>\n",
       "      <td>1.05192</td>\n",
       "      <td>-0.000361</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003799</td>\n",
       "      <td>1.375429</td>\n",
       "      <td>-0.008727</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>2.155487e-04</td>\n",
       "      <td>0.000862</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.362481e-17</td>\n",
       "      <td>-1.457915e-04</td>\n",
       "      <td>-0.000044</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>-0.000665</td>\n",
       "      <td>0.000863</td>\n",
       "      <td>0.000859</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.000847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05 04:00:00</th>\n",
       "      <td>1.05204</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003922</td>\n",
       "      <td>1.357637</td>\n",
       "      <td>-0.008840</td>\n",
       "      <td>0.001920</td>\n",
       "      <td>1.711401e-04</td>\n",
       "      <td>0.000852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.155487e-04</td>\n",
       "      <td>-1.362481e-17</td>\n",
       "      <td>-0.000146</td>\n",
       "      <td>-0.000044</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000862</td>\n",
       "      <td>0.000863</td>\n",
       "      <td>0.000859</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>0.000847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30 23:00:00</th>\n",
       "      <td>1.12012</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003163</td>\n",
       "      <td>0.116062</td>\n",
       "      <td>-0.001357</td>\n",
       "      <td>0.001446</td>\n",
       "      <td>5.952275e-05</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-6.844352e-05</td>\n",
       "      <td>6.545924e-05</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.000401</td>\n",
       "      <td>0.000402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30 23:20:00</th>\n",
       "      <td>1.12030</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003116</td>\n",
       "      <td>0.335572</td>\n",
       "      <td>-0.001517</td>\n",
       "      <td>0.001285</td>\n",
       "      <td>-2.975261e-05</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.952275e-05</td>\n",
       "      <td>-6.844352e-05</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.000401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30 23:40:00</th>\n",
       "      <td>1.12018</td>\n",
       "      <td>-0.000107</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003058</td>\n",
       "      <td>0.155421</td>\n",
       "      <td>-0.001410</td>\n",
       "      <td>0.001393</td>\n",
       "      <td>4.463867e-05</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.975261e-05</td>\n",
       "      <td>5.952275e-05</td>\n",
       "      <td>-0.000068</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.000402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 00:00:00</th>\n",
       "      <td>1.12013</td>\n",
       "      <td>-0.000045</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002986</td>\n",
       "      <td>0.090286</td>\n",
       "      <td>-0.001366</td>\n",
       "      <td>0.001437</td>\n",
       "      <td>2.975858e-06</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.463867e-05</td>\n",
       "      <td>-2.975261e-05</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>-0.000068</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.000406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 00:20:00</th>\n",
       "      <td>1.12030</td>\n",
       "      <td>0.000152</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002913</td>\n",
       "      <td>0.327692</td>\n",
       "      <td>-0.001517</td>\n",
       "      <td>0.001285</td>\n",
       "      <td>-2.684304e-17</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.975858e-06</td>\n",
       "      <td>4.463867e-05</td>\n",
       "      <td>-0.000030</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>-0.000068</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>55631 rows × 44 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:54:07.195575Z",
     "start_time": "2024-08-24T00:54:07.189809Z"
    }
   },
   "cell_type": "code",
   "source": "len(cols)",
   "id": "f12075a2f116fbfc",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:54:08.798831Z",
     "start_time": "2024-08-24T00:54:08.789726Z"
    }
   },
   "cell_type": "code",
   "source": "len(data)",
   "id": "6c3c52f1c8addda6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55631"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:54:09.895882Z",
     "start_time": "2024-08-24T00:54:09.869686Z"
    }
   },
   "cell_type": "code",
   "source": [
    "split = int(len(data)* 0.3)\n",
    "train = data.iloc[:split].copy()\n",
    "train"
   ],
   "id": "3f1399e08ce7f532",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                     EUR_USD   returns  dir       sma      boll       min  \\\n",
       "time                                                                        \n",
       "2017-01-05 02:40:00  1.05124 -0.001008    0  0.003386  1.322766 -0.008086   \n",
       "2017-01-05 03:00:00  1.05150  0.000247    1  0.003512  1.355157 -0.008331   \n",
       "2017-01-05 03:20:00  1.05230  0.000761    1  0.003652  1.557089 -0.009085   \n",
       "2017-01-05 03:40:00  1.05192 -0.000361    0  0.003799  1.375429 -0.008727   \n",
       "2017-01-05 04:00:00  1.05204  0.000114    1  0.003922  1.357637 -0.008840   \n",
       "...                      ...       ...  ...       ...       ...       ...   \n",
       "2017-11-24 20:00:00  1.19266  0.000109    1  0.003099  1.243894 -0.007295   \n",
       "2017-11-24 20:20:00  1.19267  0.000008    1  0.003160  1.195876 -0.007303   \n",
       "2017-11-24 20:40:00  1.19268  0.000008    1  0.003228  1.150992 -0.007311   \n",
       "2017-11-24 21:00:00  1.19309  0.000344    1  0.003297  1.216604 -0.007652   \n",
       "2017-11-24 21:20:00  1.19394  0.000712    1  0.003379  1.394339 -0.008359   \n",
       "\n",
       "                          max           mom       vol  dir_lag_1  ...  \\\n",
       "time                                                              ...   \n",
       "2017-01-05 02:40:00  0.002683 -4.438906e-05  0.000860        1.0  ...   \n",
       "2017-01-05 03:00:00  0.002435 -1.457915e-04  0.000859        0.0  ...   \n",
       "2017-01-05 03:20:00  0.001673 -1.362481e-17  0.000863        1.0  ...   \n",
       "2017-01-05 03:40:00  0.002034  2.155487e-04  0.000862        1.0  ...   \n",
       "2017-01-05 04:00:00  0.001920  1.711401e-04  0.000852        0.0  ...   \n",
       "...                       ...           ...       ...        ...  ...   \n",
       "2017-11-24 20:00:00  0.001098  3.354016e-05  0.000413        1.0  ...   \n",
       "2017-11-24 20:20:00  0.001090  6.428774e-05  0.000412        1.0  ...   \n",
       "2017-11-24 20:40:00  0.001082  4.192503e-05  0.000407        1.0  ...   \n",
       "2017-11-24 21:00:00  0.000738  1.201579e-04  0.000407        1.0  ...   \n",
       "2017-11-24 21:20:00  0.000025  3.547571e-04  0.000414        1.0  ...   \n",
       "\n",
       "                        mom_lag_1     mom_lag_2  mom_lag_3  mom_lag_4  \\\n",
       "time                                                                    \n",
       "2017-01-05 02:40:00  1.267090e-05 -6.647612e-04  -0.000273  -0.000101   \n",
       "2017-01-05 03:00:00 -4.438906e-05  1.267090e-05  -0.000665  -0.000273   \n",
       "2017-01-05 03:20:00 -1.457915e-04 -4.438906e-05   0.000013  -0.000665   \n",
       "2017-01-05 03:40:00 -1.362481e-17 -1.457915e-04  -0.000044   0.000013   \n",
       "2017-01-05 04:00:00  2.155487e-04 -1.362481e-17  -0.000146  -0.000044   \n",
       "...                           ...           ...        ...        ...   \n",
       "2017-11-24 20:00:00 -8.385428e-06 -5.590309e-05  -0.000061  -0.000145   \n",
       "2017-11-24 20:20:00  3.354016e-05 -8.385428e-06  -0.000056  -0.000061   \n",
       "2017-11-24 20:40:00  6.428774e-05  3.354016e-05  -0.000008  -0.000056   \n",
       "2017-11-24 21:00:00  4.192503e-05  6.428774e-05   0.000034  -0.000008   \n",
       "2017-11-24 21:20:00  1.201579e-04  4.192503e-05   0.000064   0.000034   \n",
       "\n",
       "                     mom_lag_5  vol_lag_1  vol_lag_2  vol_lag_3  vol_lag_4  \\\n",
       "time                                                                         \n",
       "2017-01-05 02:40:00   0.001385   0.000847   0.000847   0.000848   0.000841   \n",
       "2017-01-05 03:00:00  -0.000101   0.000860   0.000847   0.000847   0.000848   \n",
       "2017-01-05 03:20:00  -0.000273   0.000859   0.000860   0.000847   0.000847   \n",
       "2017-01-05 03:40:00  -0.000665   0.000863   0.000859   0.000860   0.000847   \n",
       "2017-01-05 04:00:00   0.000013   0.000862   0.000863   0.000859   0.000860   \n",
       "...                        ...        ...        ...        ...        ...   \n",
       "2017-11-24 20:00:00  -0.000282   0.000415   0.000415   0.000414   0.000414   \n",
       "2017-11-24 20:20:00  -0.000145   0.000413   0.000415   0.000415   0.000414   \n",
       "2017-11-24 20:40:00  -0.000061   0.000412   0.000413   0.000415   0.000415   \n",
       "2017-11-24 21:00:00  -0.000056   0.000407   0.000412   0.000413   0.000415   \n",
       "2017-11-24 21:20:00  -0.000008   0.000407   0.000407   0.000412   0.000413   \n",
       "\n",
       "                     vol_lag_5  \n",
       "time                            \n",
       "2017-01-05 02:40:00   0.000803  \n",
       "2017-01-05 03:00:00   0.000841  \n",
       "2017-01-05 03:20:00   0.000848  \n",
       "2017-01-05 03:40:00   0.000847  \n",
       "2017-01-05 04:00:00   0.000847  \n",
       "...                        ...  \n",
       "2017-11-24 20:00:00   0.000413  \n",
       "2017-11-24 20:20:00   0.000414  \n",
       "2017-11-24 20:40:00   0.000414  \n",
       "2017-11-24 21:00:00   0.000415  \n",
       "2017-11-24 21:20:00   0.000415  \n",
       "\n",
       "[16689 rows x 44 columns]"
      ],
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EUR_USD</th>\n",
       "      <th>returns</th>\n",
       "      <th>dir</th>\n",
       "      <th>sma</th>\n",
       "      <th>boll</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mom</th>\n",
       "      <th>vol</th>\n",
       "      <th>dir_lag_1</th>\n",
       "      <th>...</th>\n",
       "      <th>mom_lag_1</th>\n",
       "      <th>mom_lag_2</th>\n",
       "      <th>mom_lag_3</th>\n",
       "      <th>mom_lag_4</th>\n",
       "      <th>mom_lag_5</th>\n",
       "      <th>vol_lag_1</th>\n",
       "      <th>vol_lag_2</th>\n",
       "      <th>vol_lag_3</th>\n",
       "      <th>vol_lag_4</th>\n",
       "      <th>vol_lag_5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-05 02:40:00</th>\n",
       "      <td>1.05124</td>\n",
       "      <td>-0.001008</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003386</td>\n",
       "      <td>1.322766</td>\n",
       "      <td>-0.008086</td>\n",
       "      <td>0.002683</td>\n",
       "      <td>-4.438906e-05</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.267090e-05</td>\n",
       "      <td>-6.647612e-04</td>\n",
       "      <td>-0.000273</td>\n",
       "      <td>-0.000101</td>\n",
       "      <td>0.001385</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.000848</td>\n",
       "      <td>0.000841</td>\n",
       "      <td>0.000803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05 03:00:00</th>\n",
       "      <td>1.05150</td>\n",
       "      <td>0.000247</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003512</td>\n",
       "      <td>1.355157</td>\n",
       "      <td>-0.008331</td>\n",
       "      <td>0.002435</td>\n",
       "      <td>-1.457915e-04</td>\n",
       "      <td>0.000859</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.438906e-05</td>\n",
       "      <td>1.267090e-05</td>\n",
       "      <td>-0.000665</td>\n",
       "      <td>-0.000273</td>\n",
       "      <td>-0.000101</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.000848</td>\n",
       "      <td>0.000841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05 03:20:00</th>\n",
       "      <td>1.05230</td>\n",
       "      <td>0.000761</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003652</td>\n",
       "      <td>1.557089</td>\n",
       "      <td>-0.009085</td>\n",
       "      <td>0.001673</td>\n",
       "      <td>-1.362481e-17</td>\n",
       "      <td>0.000863</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.457915e-04</td>\n",
       "      <td>-4.438906e-05</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>-0.000665</td>\n",
       "      <td>-0.000273</td>\n",
       "      <td>0.000859</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.000848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05 03:40:00</th>\n",
       "      <td>1.05192</td>\n",
       "      <td>-0.000361</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003799</td>\n",
       "      <td>1.375429</td>\n",
       "      <td>-0.008727</td>\n",
       "      <td>0.002034</td>\n",
       "      <td>2.155487e-04</td>\n",
       "      <td>0.000862</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.362481e-17</td>\n",
       "      <td>-1.457915e-04</td>\n",
       "      <td>-0.000044</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>-0.000665</td>\n",
       "      <td>0.000863</td>\n",
       "      <td>0.000859</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>0.000847</td>\n",
       "      <td>0.000847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05 04:00:00</th>\n",
       "      <td>1.05204</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003922</td>\n",
       "      <td>1.357637</td>\n",
       "      <td>-0.008840</td>\n",
       "      <td>0.001920</td>\n",
       "      <td>1.711401e-04</td>\n",
       "      <td>0.000852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.155487e-04</td>\n",
       "      <td>-1.362481e-17</td>\n",
       "      <td>-0.000146</td>\n",
       "      <td>-0.000044</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>0.000862</td>\n",
       "      <td>0.000863</td>\n",
       "      <td>0.000859</td>\n",
       "      <td>0.000860</td>\n",
       "      <td>0.000847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24 20:00:00</th>\n",
       "      <td>1.19266</td>\n",
       "      <td>0.000109</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003099</td>\n",
       "      <td>1.243894</td>\n",
       "      <td>-0.007295</td>\n",
       "      <td>0.001098</td>\n",
       "      <td>3.354016e-05</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-8.385428e-06</td>\n",
       "      <td>-5.590309e-05</td>\n",
       "      <td>-0.000061</td>\n",
       "      <td>-0.000145</td>\n",
       "      <td>-0.000282</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000414</td>\n",
       "      <td>0.000414</td>\n",
       "      <td>0.000413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24 20:20:00</th>\n",
       "      <td>1.19267</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003160</td>\n",
       "      <td>1.195876</td>\n",
       "      <td>-0.007303</td>\n",
       "      <td>0.001090</td>\n",
       "      <td>6.428774e-05</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.354016e-05</td>\n",
       "      <td>-8.385428e-06</td>\n",
       "      <td>-0.000056</td>\n",
       "      <td>-0.000061</td>\n",
       "      <td>-0.000145</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000414</td>\n",
       "      <td>0.000414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24 20:40:00</th>\n",
       "      <td>1.19268</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003228</td>\n",
       "      <td>1.150992</td>\n",
       "      <td>-0.007311</td>\n",
       "      <td>0.001082</td>\n",
       "      <td>4.192503e-05</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>6.428774e-05</td>\n",
       "      <td>3.354016e-05</td>\n",
       "      <td>-0.000008</td>\n",
       "      <td>-0.000056</td>\n",
       "      <td>-0.000061</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24 21:00:00</th>\n",
       "      <td>1.19309</td>\n",
       "      <td>0.000344</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003297</td>\n",
       "      <td>1.216604</td>\n",
       "      <td>-0.007652</td>\n",
       "      <td>0.000738</td>\n",
       "      <td>1.201579e-04</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.192503e-05</td>\n",
       "      <td>6.428774e-05</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>-0.000008</td>\n",
       "      <td>-0.000056</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24 21:20:00</th>\n",
       "      <td>1.19394</td>\n",
       "      <td>0.000712</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003379</td>\n",
       "      <td>1.394339</td>\n",
       "      <td>-0.008359</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>3.547571e-04</td>\n",
       "      <td>0.000414</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.201579e-04</td>\n",
       "      <td>4.192503e-05</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>-0.000008</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.000415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16689 rows × 44 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:54:12.106740Z",
     "start_time": "2024-08-24T00:54:12.066230Z"
    }
   },
   "cell_type": "code",
   "source": [
    "test = data.iloc[split:].copy()\n",
    "test"
   ],
   "id": "def6df5d06158e67",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                     EUR_USD   returns  dir       sma      boll       min  \\\n",
       "time                                                                        \n",
       "2017-11-24 21:40:00  1.19350 -0.000369    0  0.003468  1.219148 -0.007993   \n",
       "2017-11-24 22:00:00  1.19325 -0.000209    0  0.003556  1.104014 -0.007785   \n",
       "2017-11-26 22:20:00  1.19320 -0.000042    0  0.003640  1.047758 -0.007744   \n",
       "2017-11-26 22:40:00  1.19307 -0.000109    0  0.003722  0.972170 -0.007636   \n",
       "2017-11-26 23:00:00  1.19354  0.000394    1  0.003810  1.062472 -0.008027   \n",
       "...                      ...       ...  ...       ...       ...       ...   \n",
       "2019-12-30 23:00:00  1.12012  0.000080    1  0.003163  0.116062 -0.001357   \n",
       "2019-12-30 23:20:00  1.12030  0.000161    1  0.003116  0.335572 -0.001517   \n",
       "2019-12-30 23:40:00  1.12018 -0.000107    0  0.003058  0.155421 -0.001410   \n",
       "2019-12-31 00:00:00  1.12013 -0.000045    0  0.002986  0.090286 -0.001366   \n",
       "2019-12-31 00:20:00  1.12030  0.000152    1  0.002913  0.327692 -0.001517   \n",
       "\n",
       "                          max           mom       vol  dir_lag_1  ...  \\\n",
       "time                                                              ...   \n",
       "2017-11-24 21:40:00  0.000394  2.290970e-04  0.000415        1.0  ...   \n",
       "2017-11-24 22:00:00  0.000603  4.469886e-05  0.000418        0.0  ...   \n",
       "2017-11-26 22:20:00  0.000645 -2.066629e-04  0.000418        0.0  ...   \n",
       "2017-11-26 22:40:00  0.000754 -1.201166e-04  0.000419        0.0  ...   \n",
       "2017-11-26 23:00:00  0.000360  8.100140e-05  0.000420        0.0  ...   \n",
       "...                       ...           ...       ...        ...  ...   \n",
       "2019-12-30 23:00:00  0.001446  5.952275e-05  0.000407        0.0  ...   \n",
       "2019-12-30 23:20:00  0.001285 -2.975261e-05  0.000396        1.0  ...   \n",
       "2019-12-30 23:40:00  0.001393  4.463867e-05  0.000395        1.0  ...   \n",
       "2019-12-31 00:00:00  0.001437  2.975858e-06  0.000385        0.0  ...   \n",
       "2019-12-31 00:20:00  0.001285 -2.684304e-17  0.000385        0.0  ...   \n",
       "\n",
       "                     mom_lag_1  mom_lag_2  mom_lag_3  mom_lag_4  mom_lag_5  \\\n",
       "time                                                                         \n",
       "2017-11-24 21:40:00   0.000355   0.000120   0.000042   0.000064   0.000034   \n",
       "2017-11-24 22:00:00   0.000229   0.000355   0.000120   0.000042   0.000064   \n",
       "2017-11-26 22:20:00   0.000045   0.000229   0.000355   0.000120   0.000042   \n",
       "2017-11-26 22:40:00  -0.000207   0.000045   0.000229   0.000355   0.000120   \n",
       "2017-11-26 23:00:00  -0.000120  -0.000207   0.000045   0.000229   0.000355   \n",
       "...                        ...        ...        ...        ...        ...   \n",
       "2019-12-30 23:00:00  -0.000068   0.000065   0.000021   0.000104   0.000098   \n",
       "2019-12-30 23:20:00   0.000060  -0.000068   0.000065   0.000021   0.000104   \n",
       "2019-12-30 23:40:00  -0.000030   0.000060  -0.000068   0.000065   0.000021   \n",
       "2019-12-31 00:00:00   0.000045  -0.000030   0.000060  -0.000068   0.000065   \n",
       "2019-12-31 00:20:00   0.000003   0.000045  -0.000030   0.000060  -0.000068   \n",
       "\n",
       "                     vol_lag_1  vol_lag_2  vol_lag_3  vol_lag_4  vol_lag_5  \n",
       "time                                                                        \n",
       "2017-11-24 21:40:00   0.000414   0.000407   0.000407   0.000412   0.000413  \n",
       "2017-11-24 22:00:00   0.000415   0.000414   0.000407   0.000407   0.000412  \n",
       "2017-11-26 22:20:00   0.000418   0.000415   0.000414   0.000407   0.000407  \n",
       "2017-11-26 22:40:00   0.000418   0.000418   0.000415   0.000414   0.000407  \n",
       "2017-11-26 23:00:00   0.000419   0.000418   0.000418   0.000415   0.000414  \n",
       "...                        ...        ...        ...        ...        ...  \n",
       "2019-12-30 23:00:00   0.000408   0.000406   0.000402   0.000401   0.000402  \n",
       "2019-12-30 23:20:00   0.000407   0.000408   0.000406   0.000402   0.000401  \n",
       "2019-12-30 23:40:00   0.000396   0.000407   0.000408   0.000406   0.000402  \n",
       "2019-12-31 00:00:00   0.000395   0.000396   0.000407   0.000408   0.000406  \n",
       "2019-12-31 00:20:00   0.000385   0.000395   0.000396   0.000407   0.000408  \n",
       "\n",
       "[38942 rows x 44 columns]"
      ],
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EUR_USD</th>\n",
       "      <th>returns</th>\n",
       "      <th>dir</th>\n",
       "      <th>sma</th>\n",
       "      <th>boll</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mom</th>\n",
       "      <th>vol</th>\n",
       "      <th>dir_lag_1</th>\n",
       "      <th>...</th>\n",
       "      <th>mom_lag_1</th>\n",
       "      <th>mom_lag_2</th>\n",
       "      <th>mom_lag_3</th>\n",
       "      <th>mom_lag_4</th>\n",
       "      <th>mom_lag_5</th>\n",
       "      <th>vol_lag_1</th>\n",
       "      <th>vol_lag_2</th>\n",
       "      <th>vol_lag_3</th>\n",
       "      <th>vol_lag_4</th>\n",
       "      <th>vol_lag_5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-24 21:40:00</th>\n",
       "      <td>1.19350</td>\n",
       "      <td>-0.000369</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003468</td>\n",
       "      <td>1.219148</td>\n",
       "      <td>-0.007993</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>2.290970e-04</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000414</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>0.000413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24 22:00:00</th>\n",
       "      <td>1.19325</td>\n",
       "      <td>-0.000209</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003556</td>\n",
       "      <td>1.104014</td>\n",
       "      <td>-0.007785</td>\n",
       "      <td>0.000603</td>\n",
       "      <td>4.469886e-05</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000414</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-26 22:20:00</th>\n",
       "      <td>1.19320</td>\n",
       "      <td>-0.000042</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003640</td>\n",
       "      <td>1.047758</td>\n",
       "      <td>-0.007744</td>\n",
       "      <td>0.000645</td>\n",
       "      <td>-2.066629e-04</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000414</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-26 22:40:00</th>\n",
       "      <td>1.19307</td>\n",
       "      <td>-0.000109</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003722</td>\n",
       "      <td>0.972170</td>\n",
       "      <td>-0.007636</td>\n",
       "      <td>0.000754</td>\n",
       "      <td>-1.201166e-04</td>\n",
       "      <td>0.000419</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000207</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000414</td>\n",
       "      <td>0.000407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-26 23:00:00</th>\n",
       "      <td>1.19354</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003810</td>\n",
       "      <td>1.062472</td>\n",
       "      <td>-0.008027</td>\n",
       "      <td>0.000360</td>\n",
       "      <td>8.100140e-05</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000120</td>\n",
       "      <td>-0.000207</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.000419</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30 23:00:00</th>\n",
       "      <td>1.12012</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003163</td>\n",
       "      <td>0.116062</td>\n",
       "      <td>-0.001357</td>\n",
       "      <td>0.001446</td>\n",
       "      <td>5.952275e-05</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000068</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.000401</td>\n",
       "      <td>0.000402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30 23:20:00</th>\n",
       "      <td>1.12030</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003116</td>\n",
       "      <td>0.335572</td>\n",
       "      <td>-0.001517</td>\n",
       "      <td>0.001285</td>\n",
       "      <td>-2.975261e-05</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>-0.000068</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.000401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30 23:40:00</th>\n",
       "      <td>1.12018</td>\n",
       "      <td>-0.000107</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003058</td>\n",
       "      <td>0.155421</td>\n",
       "      <td>-0.001410</td>\n",
       "      <td>0.001393</td>\n",
       "      <td>4.463867e-05</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000030</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>-0.000068</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.000402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 00:00:00</th>\n",
       "      <td>1.12013</td>\n",
       "      <td>-0.000045</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002986</td>\n",
       "      <td>0.090286</td>\n",
       "      <td>-0.001366</td>\n",
       "      <td>0.001437</td>\n",
       "      <td>2.975858e-06</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>-0.000030</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>-0.000068</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.000406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 00:20:00</th>\n",
       "      <td>1.12030</td>\n",
       "      <td>0.000152</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002913</td>\n",
       "      <td>0.327692</td>\n",
       "      <td>-0.001517</td>\n",
       "      <td>0.001285</td>\n",
       "      <td>-2.684304e-17</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>-0.000030</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>-0.000068</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38942 rows × 44 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:54:13.959921Z",
     "start_time": "2024-08-24T00:54:13.939448Z"
    }
   },
   "cell_type": "code",
   "source": "mu, std = train.mean(), train.std()",
   "id": "fab9606e3c39257f",
   "outputs": [],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:54:15.012018Z",
     "start_time": "2024-08-24T00:54:15.000982Z"
    }
   },
   "cell_type": "code",
   "source": "mu",
   "id": "bf8c258cb223552",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EUR_USD       1.124892\n",
       "returns       0.000008\n",
       "dir           0.496435\n",
       "sma           0.000428\n",
       "boll          0.054613\n",
       "min          -0.002632\n",
       "max           0.002242\n",
       "mom           0.000008\n",
       "vol           0.000507\n",
       "dir_lag_1     0.496435\n",
       "dir_lag_2     0.496435\n",
       "dir_lag_3     0.496375\n",
       "dir_lag_4     0.496315\n",
       "dir_lag_5     0.496315\n",
       "sma_lag_1     0.000428\n",
       "sma_lag_2     0.000428\n",
       "sma_lag_3     0.000428\n",
       "sma_lag_4     0.000427\n",
       "sma_lag_5     0.000427\n",
       "boll_lag_1    0.054633\n",
       "boll_lag_2    0.054664\n",
       "boll_lag_3    0.054691\n",
       "boll_lag_4    0.054739\n",
       "boll_lag_5    0.054831\n",
       "min_lag_1    -0.002632\n",
       "min_lag_2    -0.002632\n",
       "min_lag_3    -0.002632\n",
       "min_lag_4    -0.002633\n",
       "min_lag_5    -0.002633\n",
       "max_lag_1     0.002242\n",
       "max_lag_2     0.002242\n",
       "max_lag_3     0.002243\n",
       "max_lag_4     0.002243\n",
       "max_lag_5     0.002243\n",
       "mom_lag_1     0.000008\n",
       "mom_lag_2     0.000007\n",
       "mom_lag_3     0.000007\n",
       "mom_lag_4     0.000007\n",
       "mom_lag_5     0.000008\n",
       "vol_lag_1     0.000507\n",
       "vol_lag_2     0.000507\n",
       "vol_lag_3     0.000507\n",
       "vol_lag_4     0.000507\n",
       "vol_lag_5     0.000507\n",
       "dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:54:16.711087Z",
     "start_time": "2024-08-24T00:54:16.705413Z"
    }
   },
   "cell_type": "code",
   "source": "std",
   "id": "4b0d1b00ee91686f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EUR_USD       0.049849\n",
       "returns       0.000544\n",
       "dir           0.500002\n",
       "sma           0.002789\n",
       "boll          1.439775\n",
       "min           0.002599\n",
       "max           0.002097\n",
       "mom           0.000310\n",
       "vol           0.000200\n",
       "dir_lag_1     0.500002\n",
       "dir_lag_2     0.500002\n",
       "dir_lag_3     0.500002\n",
       "dir_lag_4     0.500001\n",
       "dir_lag_5     0.500001\n",
       "sma_lag_1     0.002789\n",
       "sma_lag_2     0.002789\n",
       "sma_lag_3     0.002789\n",
       "sma_lag_4     0.002789\n",
       "sma_lag_5     0.002789\n",
       "boll_lag_1    1.439797\n",
       "boll_lag_2    1.439827\n",
       "boll_lag_3    1.439852\n",
       "boll_lag_4    1.439903\n",
       "boll_lag_5    1.440028\n",
       "min_lag_1     0.002599\n",
       "min_lag_2     0.002599\n",
       "min_lag_3     0.002599\n",
       "min_lag_4     0.002599\n",
       "min_lag_5     0.002600\n",
       "max_lag_1     0.002097\n",
       "max_lag_2     0.002097\n",
       "max_lag_3     0.002097\n",
       "max_lag_4     0.002097\n",
       "max_lag_5     0.002097\n",
       "mom_lag_1     0.000310\n",
       "mom_lag_2     0.000310\n",
       "mom_lag_3     0.000310\n",
       "mom_lag_4     0.000310\n",
       "mom_lag_5     0.000310\n",
       "vol_lag_1     0.000200\n",
       "vol_lag_2     0.000200\n",
       "vol_lag_3     0.000200\n",
       "vol_lag_4     0.000200\n",
       "vol_lag_5     0.000200\n",
       "dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:54:18.544532Z",
     "start_time": "2024-08-24T00:54:18.533903Z"
    }
   },
   "cell_type": "code",
   "source": "train_standardized = (train-mu) / std",
   "id": "a1263cf64c4a0d69",
   "outputs": [],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:54:19.800926Z",
     "start_time": "2024-08-24T00:54:19.769386Z"
    }
   },
   "cell_type": "code",
   "source": "train_standardized",
   "id": "8ad2b3000aabc7c6",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                      EUR_USD   returns       dir       sma      boll  \\\n",
       "time                                                                    \n",
       "2017-01-05 02:40:00 -1.477520 -1.866357 -0.992865  1.060553  0.880800   \n",
       "2017-01-05 03:00:00 -1.472304  0.440639  1.007126  1.105823  0.903297   \n",
       "2017-01-05 03:20:00 -1.456256  1.383994  1.007126  1.155968  1.043549   \n",
       "2017-01-05 03:40:00 -1.463879 -0.677779 -0.992865  1.208766  0.917377   \n",
       "2017-01-05 04:00:00 -1.461472  0.195761  1.007126  1.252888  0.905019   \n",
       "...                       ...       ...       ...       ...       ...   \n",
       "2017-11-24 20:00:00  1.359466  0.186452  1.007126  0.957705  0.826019   \n",
       "2017-11-24 20:20:00  1.359667  0.001503  1.007126  0.979790  0.792668   \n",
       "2017-11-24 20:40:00  1.359868  0.001503  1.007126  1.004146  0.761493   \n",
       "2017-11-24 21:00:00  1.368092  0.617844  1.007126  1.028931  0.807064   \n",
       "2017-11-24 21:20:00  1.385144  1.295130  1.007126  1.058043  0.930510   \n",
       "\n",
       "                          min       max       mom       vol  dir_lag_1  ...  \\\n",
       "time                                                                    ...   \n",
       "2017-01-05 02:40:00 -2.098622  0.209953 -0.167541  1.766221   1.007126  ...   \n",
       "2017-01-05 03:00:00 -2.193007  0.091721 -0.494603  1.757521  -0.992865  ...   \n",
       "2017-01-05 03:20:00 -2.483131 -0.271703 -0.024369  1.778937   1.007126  ...   \n",
       "2017-01-05 03:40:00 -2.345377 -0.099146  0.670859  1.775613   1.007126  ...   \n",
       "2017-01-05 04:00:00 -2.388889 -0.153651  0.527624  1.722398  -0.992865  ...   \n",
       "...                       ...       ...       ...       ...        ...  ...   \n",
       "2017-11-24 20:00:00 -1.794196 -0.545499  0.083811 -0.472544   1.007126  ...   \n",
       "2017-11-24 20:20:00 -1.797399 -0.549501  0.182984 -0.475027   1.007126  ...   \n",
       "2017-11-24 20:40:00 -1.800602 -0.553504  0.110856 -0.502223   1.007126  ...   \n",
       "2017-11-24 21:00:00 -1.931879 -0.717558  0.363187 -0.500673   1.007126  ...   \n",
       "2017-11-24 21:20:00 -2.203753 -1.057312  1.119859 -0.464865   1.007126  ...   \n",
       "\n",
       "                     mom_lag_1  mom_lag_2  mom_lag_3  mom_lag_4  mom_lag_5  \\\n",
       "time                                                                         \n",
       "2017-01-05 02:40:00   0.016567  -2.168046  -0.903045  -0.350919   4.439110   \n",
       "2017-01-05 03:00:00  -0.167481   0.016716  -2.167934  -0.903010  -0.350972   \n",
       "2017-01-05 03:20:00  -0.494555  -0.167306   0.016776  -2.167896  -0.902738   \n",
       "2017-01-05 03:40:00  -0.024304  -0.494336  -0.167241   0.016808  -2.166877   \n",
       "2017-01-05 04:00:00   0.670950  -0.024149  -0.494263  -0.167209   0.016537   \n",
       "...                        ...        ...        ...        ...        ...   \n",
       "2017-11-24 20:00:00  -0.051351  -0.204440  -0.222384  -0.492689  -0.933749   \n",
       "2017-11-24 20:20:00   0.083880  -0.051192  -0.204374  -0.222352  -0.492659   \n",
       "2017-11-24 20:40:00   0.183057   0.084021  -0.051130  -0.204341  -0.222481   \n",
       "2017-11-24 21:00:00   0.110926   0.183184   0.084079  -0.051098  -0.204482   \n",
       "2017-11-24 21:20:00   0.363266   0.111062   0.183240   0.084111  -0.051329   \n",
       "\n",
       "                     vol_lag_1  vol_lag_2  vol_lag_3  vol_lag_4  vol_lag_5  \n",
       "time                                                                        \n",
       "2017-01-05 02:40:00   1.697317   1.696388   1.703940   1.668468   1.477845  \n",
       "2017-01-05 03:00:00   1.765950   1.697052   1.696121   1.703681   1.668253  \n",
       "2017-01-05 03:20:00   1.757251   1.765679   1.696785   1.695862   1.703464  \n",
       "2017-01-05 03:40:00   1.778665   1.756981   1.765407   1.696526   1.695646  \n",
       "2017-01-05 04:00:00   1.775342   1.778394   1.756709   1.765143   1.696310  \n",
       "...                        ...        ...        ...        ...        ...  \n",
       "2017-11-24 20:00:00  -0.463523  -0.461655  -0.466881  -0.469420  -0.472963  \n",
       "2017-11-24 20:20:00  -0.472636  -0.463618  -0.461750  -0.466974  -0.469509  \n",
       "2017-11-24 20:40:00  -0.475119  -0.472730  -0.463713  -0.461843  -0.467063  \n",
       "2017-11-24 21:00:00  -0.502312  -0.475213  -0.472825  -0.463806  -0.461933  \n",
       "2017-11-24 21:20:00  -0.500763  -0.502404  -0.475307  -0.472917  -0.463896  \n",
       "\n",
       "[16689 rows x 44 columns]"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EUR_USD</th>\n",
       "      <th>returns</th>\n",
       "      <th>dir</th>\n",
       "      <th>sma</th>\n",
       "      <th>boll</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mom</th>\n",
       "      <th>vol</th>\n",
       "      <th>dir_lag_1</th>\n",
       "      <th>...</th>\n",
       "      <th>mom_lag_1</th>\n",
       "      <th>mom_lag_2</th>\n",
       "      <th>mom_lag_3</th>\n",
       "      <th>mom_lag_4</th>\n",
       "      <th>mom_lag_5</th>\n",
       "      <th>vol_lag_1</th>\n",
       "      <th>vol_lag_2</th>\n",
       "      <th>vol_lag_3</th>\n",
       "      <th>vol_lag_4</th>\n",
       "      <th>vol_lag_5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-05 02:40:00</th>\n",
       "      <td>-1.477520</td>\n",
       "      <td>-1.866357</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>1.060553</td>\n",
       "      <td>0.880800</td>\n",
       "      <td>-2.098622</td>\n",
       "      <td>0.209953</td>\n",
       "      <td>-0.167541</td>\n",
       "      <td>1.766221</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016567</td>\n",
       "      <td>-2.168046</td>\n",
       "      <td>-0.903045</td>\n",
       "      <td>-0.350919</td>\n",
       "      <td>4.439110</td>\n",
       "      <td>1.697317</td>\n",
       "      <td>1.696388</td>\n",
       "      <td>1.703940</td>\n",
       "      <td>1.668468</td>\n",
       "      <td>1.477845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05 03:00:00</th>\n",
       "      <td>-1.472304</td>\n",
       "      <td>0.440639</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>1.105823</td>\n",
       "      <td>0.903297</td>\n",
       "      <td>-2.193007</td>\n",
       "      <td>0.091721</td>\n",
       "      <td>-0.494603</td>\n",
       "      <td>1.757521</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.167481</td>\n",
       "      <td>0.016716</td>\n",
       "      <td>-2.167934</td>\n",
       "      <td>-0.903010</td>\n",
       "      <td>-0.350972</td>\n",
       "      <td>1.765950</td>\n",
       "      <td>1.697052</td>\n",
       "      <td>1.696121</td>\n",
       "      <td>1.703681</td>\n",
       "      <td>1.668253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05 03:20:00</th>\n",
       "      <td>-1.456256</td>\n",
       "      <td>1.383994</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>1.155968</td>\n",
       "      <td>1.043549</td>\n",
       "      <td>-2.483131</td>\n",
       "      <td>-0.271703</td>\n",
       "      <td>-0.024369</td>\n",
       "      <td>1.778937</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.494555</td>\n",
       "      <td>-0.167306</td>\n",
       "      <td>0.016776</td>\n",
       "      <td>-2.167896</td>\n",
       "      <td>-0.902738</td>\n",
       "      <td>1.757251</td>\n",
       "      <td>1.765679</td>\n",
       "      <td>1.696785</td>\n",
       "      <td>1.695862</td>\n",
       "      <td>1.703464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05 03:40:00</th>\n",
       "      <td>-1.463879</td>\n",
       "      <td>-0.677779</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>1.208766</td>\n",
       "      <td>0.917377</td>\n",
       "      <td>-2.345377</td>\n",
       "      <td>-0.099146</td>\n",
       "      <td>0.670859</td>\n",
       "      <td>1.775613</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.024304</td>\n",
       "      <td>-0.494336</td>\n",
       "      <td>-0.167241</td>\n",
       "      <td>0.016808</td>\n",
       "      <td>-2.166877</td>\n",
       "      <td>1.778665</td>\n",
       "      <td>1.756981</td>\n",
       "      <td>1.765407</td>\n",
       "      <td>1.696526</td>\n",
       "      <td>1.695646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05 04:00:00</th>\n",
       "      <td>-1.461472</td>\n",
       "      <td>0.195761</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>1.252888</td>\n",
       "      <td>0.905019</td>\n",
       "      <td>-2.388889</td>\n",
       "      <td>-0.153651</td>\n",
       "      <td>0.527624</td>\n",
       "      <td>1.722398</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>...</td>\n",
       "      <td>0.670950</td>\n",
       "      <td>-0.024149</td>\n",
       "      <td>-0.494263</td>\n",
       "      <td>-0.167209</td>\n",
       "      <td>0.016537</td>\n",
       "      <td>1.775342</td>\n",
       "      <td>1.778394</td>\n",
       "      <td>1.756709</td>\n",
       "      <td>1.765143</td>\n",
       "      <td>1.696310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24 20:00:00</th>\n",
       "      <td>1.359466</td>\n",
       "      <td>0.186452</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>0.957705</td>\n",
       "      <td>0.826019</td>\n",
       "      <td>-1.794196</td>\n",
       "      <td>-0.545499</td>\n",
       "      <td>0.083811</td>\n",
       "      <td>-0.472544</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051351</td>\n",
       "      <td>-0.204440</td>\n",
       "      <td>-0.222384</td>\n",
       "      <td>-0.492689</td>\n",
       "      <td>-0.933749</td>\n",
       "      <td>-0.463523</td>\n",
       "      <td>-0.461655</td>\n",
       "      <td>-0.466881</td>\n",
       "      <td>-0.469420</td>\n",
       "      <td>-0.472963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24 20:20:00</th>\n",
       "      <td>1.359667</td>\n",
       "      <td>0.001503</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>0.979790</td>\n",
       "      <td>0.792668</td>\n",
       "      <td>-1.797399</td>\n",
       "      <td>-0.549501</td>\n",
       "      <td>0.182984</td>\n",
       "      <td>-0.475027</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>...</td>\n",
       "      <td>0.083880</td>\n",
       "      <td>-0.051192</td>\n",
       "      <td>-0.204374</td>\n",
       "      <td>-0.222352</td>\n",
       "      <td>-0.492659</td>\n",
       "      <td>-0.472636</td>\n",
       "      <td>-0.463618</td>\n",
       "      <td>-0.461750</td>\n",
       "      <td>-0.466974</td>\n",
       "      <td>-0.469509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24 20:40:00</th>\n",
       "      <td>1.359868</td>\n",
       "      <td>0.001503</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>1.004146</td>\n",
       "      <td>0.761493</td>\n",
       "      <td>-1.800602</td>\n",
       "      <td>-0.553504</td>\n",
       "      <td>0.110856</td>\n",
       "      <td>-0.502223</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>...</td>\n",
       "      <td>0.183057</td>\n",
       "      <td>0.084021</td>\n",
       "      <td>-0.051130</td>\n",
       "      <td>-0.204341</td>\n",
       "      <td>-0.222481</td>\n",
       "      <td>-0.475119</td>\n",
       "      <td>-0.472730</td>\n",
       "      <td>-0.463713</td>\n",
       "      <td>-0.461843</td>\n",
       "      <td>-0.467063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24 21:00:00</th>\n",
       "      <td>1.368092</td>\n",
       "      <td>0.617844</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>1.028931</td>\n",
       "      <td>0.807064</td>\n",
       "      <td>-1.931879</td>\n",
       "      <td>-0.717558</td>\n",
       "      <td>0.363187</td>\n",
       "      <td>-0.500673</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>...</td>\n",
       "      <td>0.110926</td>\n",
       "      <td>0.183184</td>\n",
       "      <td>0.084079</td>\n",
       "      <td>-0.051098</td>\n",
       "      <td>-0.204482</td>\n",
       "      <td>-0.502312</td>\n",
       "      <td>-0.475213</td>\n",
       "      <td>-0.472825</td>\n",
       "      <td>-0.463806</td>\n",
       "      <td>-0.461933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24 21:20:00</th>\n",
       "      <td>1.385144</td>\n",
       "      <td>1.295130</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>1.058043</td>\n",
       "      <td>0.930510</td>\n",
       "      <td>-2.203753</td>\n",
       "      <td>-1.057312</td>\n",
       "      <td>1.119859</td>\n",
       "      <td>-0.464865</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>...</td>\n",
       "      <td>0.363266</td>\n",
       "      <td>0.111062</td>\n",
       "      <td>0.183240</td>\n",
       "      <td>0.084111</td>\n",
       "      <td>-0.051329</td>\n",
       "      <td>-0.500763</td>\n",
       "      <td>-0.502404</td>\n",
       "      <td>-0.475307</td>\n",
       "      <td>-0.472917</td>\n",
       "      <td>-0.463896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16689 rows × 44 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:54:21.731836Z",
     "start_time": "2024-08-24T00:54:21.659436Z"
    }
   },
   "cell_type": "code",
   "source": "train_standardized.describe()",
   "id": "d749aad2f1dc736b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            EUR_USD       returns           dir           sma          boll  \\\n",
       "count  1.668900e+04  1.668900e+04  1.668900e+04  1.668900e+04  1.668900e+04   \n",
       "mean   1.682584e-15 -5.321939e-18  8.429952e-17 -2.128776e-17 -3.406041e-18   \n",
       "std    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "min   -1.575015e+00 -1.543850e+01 -9.928650e-01 -3.788222e+00 -4.389269e+00   \n",
       "25%   -1.046014e+00 -4.458280e-01 -9.928650e-01 -6.778977e-01 -7.516934e-01   \n",
       "50%   -5.762270e-02 -1.390808e-02 -9.928650e-01 -4.572725e-02  3.196566e-02   \n",
       "75%    1.013218e+00  4.411443e-01  1.007126e+00  6.175868e-01  7.415974e-01   \n",
       "max    1.670809e+00  2.834778e+01  1.007126e+00  3.195816e+00  4.352587e+00   \n",
       "\n",
       "                min           max           mom           vol     dir_lag_1  \\\n",
       "count  1.668900e+04  1.668900e+04  1.668900e+04  1.668900e+04  1.668900e+04   \n",
       "mean   4.002098e-17 -6.130874e-17 -1.277265e-18 -2.827014e-16  8.642829e-17   \n",
       "std    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "min   -6.557869e+00 -1.069294e+00 -1.177472e+01 -1.776386e+00 -9.928650e-01   \n",
       "25%   -4.429481e-01 -7.518524e-01 -4.612796e-01 -6.304506e-01 -9.928650e-01   \n",
       "50%    3.112298e-01 -2.753571e-01 -1.512475e-02 -2.055092e-01 -9.928650e-01   \n",
       "75%    7.247818e-01  4.973353e-01  4.417874e-01  3.795033e-01  1.007126e+00   \n",
       "max    1.012976e+00  6.838293e+00  1.859157e+01  9.250276e+00  1.007126e+00   \n",
       "\n",
       "       ...     mom_lag_1     mom_lag_2     mom_lag_3     mom_lag_4  \\\n",
       "count  ...  1.668900e+04  1.668900e+04  1.668900e+04  1.668900e+04   \n",
       "mean   ...  7.663592e-18 -1.234690e-17 -4.257551e-18  5.960572e-18   \n",
       "std    ...  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "min    ... -1.177509e+01 -1.177333e+01 -1.177299e+01 -1.177292e+01   \n",
       "25%    ... -4.612308e-01 -4.610308e-01 -4.609663e-01 -4.609331e-01   \n",
       "50%    ... -1.505921e-02 -1.491578e-02 -1.486282e-02 -1.486861e-02   \n",
       "75%    ...  4.414241e-01  4.415153e-01  4.415655e-01  4.415963e-01   \n",
       "max    ...  1.859234e+01  1.858994e+01  1.858956e+01  1.858954e+01   \n",
       "\n",
       "          mom_lag_5     vol_lag_1     vol_lag_2     vol_lag_3     vol_lag_4  \\\n",
       "count  1.668900e+04  1.668900e+04  1.668900e+04  1.668900e+04  1.668900e+04   \n",
       "mean   3.406041e-18  2.622652e-16  4.359733e-16  4.087249e-17 -3.610403e-16   \n",
       "std    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "min   -1.176623e+01 -1.776374e+00 -1.776365e+00 -1.776357e+00 -1.776349e+00   \n",
       "25%   -4.609219e-01 -6.305299e-01 -6.306119e-01 -6.306940e-01 -6.307741e-01   \n",
       "50%   -1.512080e-02 -2.056205e-01 -2.057108e-01 -2.057230e-01 -2.058105e-01   \n",
       "75%    4.415203e-01  3.794915e-01  3.793497e-01  3.792180e-01  3.791320e-01   \n",
       "max    1.857830e+01  9.249408e+00  9.248548e+00  9.247681e+00  9.246844e+00   \n",
       "\n",
       "          vol_lag_5  \n",
       "count  1.668900e+04  \n",
       "mean  -1.873323e-17  \n",
       "std    1.000000e+00  \n",
       "min   -1.776361e+00  \n",
       "25%   -6.308540e-01  \n",
       "50%   -2.057860e-01  \n",
       "75%    3.791341e-01  \n",
       "max    9.246184e+00  \n",
       "\n",
       "[8 rows x 44 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EUR_USD</th>\n",
       "      <th>returns</th>\n",
       "      <th>dir</th>\n",
       "      <th>sma</th>\n",
       "      <th>boll</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mom</th>\n",
       "      <th>vol</th>\n",
       "      <th>dir_lag_1</th>\n",
       "      <th>...</th>\n",
       "      <th>mom_lag_1</th>\n",
       "      <th>mom_lag_2</th>\n",
       "      <th>mom_lag_3</th>\n",
       "      <th>mom_lag_4</th>\n",
       "      <th>mom_lag_5</th>\n",
       "      <th>vol_lag_1</th>\n",
       "      <th>vol_lag_2</th>\n",
       "      <th>vol_lag_3</th>\n",
       "      <th>vol_lag_4</th>\n",
       "      <th>vol_lag_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.668900e+04</td>\n",
       "      <td>1.668900e+04</td>\n",
       "      <td>1.668900e+04</td>\n",
       "      <td>1.668900e+04</td>\n",
       "      <td>1.668900e+04</td>\n",
       "      <td>1.668900e+04</td>\n",
       "      <td>1.668900e+04</td>\n",
       "      <td>1.668900e+04</td>\n",
       "      <td>1.668900e+04</td>\n",
       "      <td>1.668900e+04</td>\n",
       "      <td>...</td>\n",
       "      <td>1.668900e+04</td>\n",
       "      <td>1.668900e+04</td>\n",
       "      <td>1.668900e+04</td>\n",
       "      <td>1.668900e+04</td>\n",
       "      <td>1.668900e+04</td>\n",
       "      <td>1.668900e+04</td>\n",
       "      <td>1.668900e+04</td>\n",
       "      <td>1.668900e+04</td>\n",
       "      <td>1.668900e+04</td>\n",
       "      <td>1.668900e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.682584e-15</td>\n",
       "      <td>-5.321939e-18</td>\n",
       "      <td>8.429952e-17</td>\n",
       "      <td>-2.128776e-17</td>\n",
       "      <td>-3.406041e-18</td>\n",
       "      <td>4.002098e-17</td>\n",
       "      <td>-6.130874e-17</td>\n",
       "      <td>-1.277265e-18</td>\n",
       "      <td>-2.827014e-16</td>\n",
       "      <td>8.642829e-17</td>\n",
       "      <td>...</td>\n",
       "      <td>7.663592e-18</td>\n",
       "      <td>-1.234690e-17</td>\n",
       "      <td>-4.257551e-18</td>\n",
       "      <td>5.960572e-18</td>\n",
       "      <td>3.406041e-18</td>\n",
       "      <td>2.622652e-16</td>\n",
       "      <td>4.359733e-16</td>\n",
       "      <td>4.087249e-17</td>\n",
       "      <td>-3.610403e-16</td>\n",
       "      <td>-1.873323e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.575015e+00</td>\n",
       "      <td>-1.543850e+01</td>\n",
       "      <td>-9.928650e-01</td>\n",
       "      <td>-3.788222e+00</td>\n",
       "      <td>-4.389269e+00</td>\n",
       "      <td>-6.557869e+00</td>\n",
       "      <td>-1.069294e+00</td>\n",
       "      <td>-1.177472e+01</td>\n",
       "      <td>-1.776386e+00</td>\n",
       "      <td>-9.928650e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.177509e+01</td>\n",
       "      <td>-1.177333e+01</td>\n",
       "      <td>-1.177299e+01</td>\n",
       "      <td>-1.177292e+01</td>\n",
       "      <td>-1.176623e+01</td>\n",
       "      <td>-1.776374e+00</td>\n",
       "      <td>-1.776365e+00</td>\n",
       "      <td>-1.776357e+00</td>\n",
       "      <td>-1.776349e+00</td>\n",
       "      <td>-1.776361e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.046014e+00</td>\n",
       "      <td>-4.458280e-01</td>\n",
       "      <td>-9.928650e-01</td>\n",
       "      <td>-6.778977e-01</td>\n",
       "      <td>-7.516934e-01</td>\n",
       "      <td>-4.429481e-01</td>\n",
       "      <td>-7.518524e-01</td>\n",
       "      <td>-4.612796e-01</td>\n",
       "      <td>-6.304506e-01</td>\n",
       "      <td>-9.928650e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.612308e-01</td>\n",
       "      <td>-4.610308e-01</td>\n",
       "      <td>-4.609663e-01</td>\n",
       "      <td>-4.609331e-01</td>\n",
       "      <td>-4.609219e-01</td>\n",
       "      <td>-6.305299e-01</td>\n",
       "      <td>-6.306119e-01</td>\n",
       "      <td>-6.306940e-01</td>\n",
       "      <td>-6.307741e-01</td>\n",
       "      <td>-6.308540e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-5.762270e-02</td>\n",
       "      <td>-1.390808e-02</td>\n",
       "      <td>-9.928650e-01</td>\n",
       "      <td>-4.572725e-02</td>\n",
       "      <td>3.196566e-02</td>\n",
       "      <td>3.112298e-01</td>\n",
       "      <td>-2.753571e-01</td>\n",
       "      <td>-1.512475e-02</td>\n",
       "      <td>-2.055092e-01</td>\n",
       "      <td>-9.928650e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.505921e-02</td>\n",
       "      <td>-1.491578e-02</td>\n",
       "      <td>-1.486282e-02</td>\n",
       "      <td>-1.486861e-02</td>\n",
       "      <td>-1.512080e-02</td>\n",
       "      <td>-2.056205e-01</td>\n",
       "      <td>-2.057108e-01</td>\n",
       "      <td>-2.057230e-01</td>\n",
       "      <td>-2.058105e-01</td>\n",
       "      <td>-2.057860e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.013218e+00</td>\n",
       "      <td>4.411443e-01</td>\n",
       "      <td>1.007126e+00</td>\n",
       "      <td>6.175868e-01</td>\n",
       "      <td>7.415974e-01</td>\n",
       "      <td>7.247818e-01</td>\n",
       "      <td>4.973353e-01</td>\n",
       "      <td>4.417874e-01</td>\n",
       "      <td>3.795033e-01</td>\n",
       "      <td>1.007126e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>4.414241e-01</td>\n",
       "      <td>4.415153e-01</td>\n",
       "      <td>4.415655e-01</td>\n",
       "      <td>4.415963e-01</td>\n",
       "      <td>4.415203e-01</td>\n",
       "      <td>3.794915e-01</td>\n",
       "      <td>3.793497e-01</td>\n",
       "      <td>3.792180e-01</td>\n",
       "      <td>3.791320e-01</td>\n",
       "      <td>3.791341e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.670809e+00</td>\n",
       "      <td>2.834778e+01</td>\n",
       "      <td>1.007126e+00</td>\n",
       "      <td>3.195816e+00</td>\n",
       "      <td>4.352587e+00</td>\n",
       "      <td>1.012976e+00</td>\n",
       "      <td>6.838293e+00</td>\n",
       "      <td>1.859157e+01</td>\n",
       "      <td>9.250276e+00</td>\n",
       "      <td>1.007126e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.859234e+01</td>\n",
       "      <td>1.858994e+01</td>\n",
       "      <td>1.858956e+01</td>\n",
       "      <td>1.858954e+01</td>\n",
       "      <td>1.857830e+01</td>\n",
       "      <td>9.249408e+00</td>\n",
       "      <td>9.248548e+00</td>\n",
       "      <td>9.247681e+00</td>\n",
       "      <td>9.246844e+00</td>\n",
       "      <td>9.246184e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 44 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:54:25.767481Z",
     "start_time": "2024-08-24T00:54:23.603294Z"
    }
   },
   "cell_type": "code",
   "source": "from DNNModel import *",
   "id": "6567ae9480f66b19",
   "outputs": [],
   "execution_count": 28
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:55:15.570098Z",
     "start_time": "2024-08-24T00:54:25.780515Z"
    }
   },
   "cell_type": "code",
   "source": [
    "set_seeds(100)\n",
    "\n",
    "model = create_model(hl=3, hu=50, dropout= True, input_dim=len(cols))\n",
    "model.fit(x=train_standardized[cols], y=train['dir'], epochs=50, verbose=True,\n",
    "          validation_split=0.2, shuffle=False, class_weight=cw(train))"
   ],
   "id": "99eeb5e0b3482dc8",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\code\\trading\\v1\\pythonProject\\.venv\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n",
      "C:\\code\\trading\\v1\\pythonProject\\.venv\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\data_adapter_utils.py:126: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  sample_weight[i] = class_weight.get(int(y[i]), 1.0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m2s\u001B[0m 2ms/step - accuracy: 0.4968 - loss: 0.7132 - val_accuracy: 0.5093 - val_loss: 0.6934\n",
      "Epoch 2/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 1ms/step - accuracy: 0.5070 - loss: 0.7044 - val_accuracy: 0.5024 - val_loss: 0.6934\n",
      "Epoch 3/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 1ms/step - accuracy: 0.4941 - loss: 0.7011 - val_accuracy: 0.5057 - val_loss: 0.6932\n",
      "Epoch 4/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5020 - loss: 0.6980 - val_accuracy: 0.5030 - val_loss: 0.6933\n",
      "Epoch 5/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 1ms/step - accuracy: 0.5076 - loss: 0.6968 - val_accuracy: 0.5024 - val_loss: 0.6933\n",
      "Epoch 6/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 1ms/step - accuracy: 0.4997 - loss: 0.6961 - val_accuracy: 0.5003 - val_loss: 0.6933\n",
      "Epoch 7/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5054 - loss: 0.6965 - val_accuracy: 0.5003 - val_loss: 0.6932\n",
      "Epoch 8/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5089 - loss: 0.6951 - val_accuracy: 0.5054 - val_loss: 0.6932\n",
      "Epoch 9/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 1ms/step - accuracy: 0.4970 - loss: 0.6948 - val_accuracy: 0.5036 - val_loss: 0.6932\n",
      "Epoch 10/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5079 - loss: 0.6954 - val_accuracy: 0.5063 - val_loss: 0.6931\n",
      "Epoch 11/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5027 - loss: 0.6939 - val_accuracy: 0.5066 - val_loss: 0.6931\n",
      "Epoch 12/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 3ms/step - accuracy: 0.5143 - loss: 0.6928 - val_accuracy: 0.5063 - val_loss: 0.6930\n",
      "Epoch 13/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 3ms/step - accuracy: 0.5118 - loss: 0.6935 - val_accuracy: 0.5057 - val_loss: 0.6931\n",
      "Epoch 14/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5168 - loss: 0.6927 - val_accuracy: 0.5033 - val_loss: 0.6931\n",
      "Epoch 15/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5129 - loss: 0.6921 - val_accuracy: 0.5018 - val_loss: 0.6932\n",
      "Epoch 16/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5137 - loss: 0.6927 - val_accuracy: 0.5069 - val_loss: 0.6932\n",
      "Epoch 17/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 3ms/step - accuracy: 0.5143 - loss: 0.6923 - val_accuracy: 0.5030 - val_loss: 0.6933\n",
      "Epoch 18/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5202 - loss: 0.6925 - val_accuracy: 0.5003 - val_loss: 0.6932\n",
      "Epoch 19/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5222 - loss: 0.6924 - val_accuracy: 0.5024 - val_loss: 0.6932\n",
      "Epoch 20/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5170 - loss: 0.6922 - val_accuracy: 0.5039 - val_loss: 0.6932\n",
      "Epoch 21/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5176 - loss: 0.6930 - val_accuracy: 0.5018 - val_loss: 0.6932\n",
      "Epoch 22/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 3ms/step - accuracy: 0.5136 - loss: 0.6930 - val_accuracy: 0.5072 - val_loss: 0.6932\n",
      "Epoch 23/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 3ms/step - accuracy: 0.5170 - loss: 0.6924 - val_accuracy: 0.5069 - val_loss: 0.6932\n",
      "Epoch 24/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 3ms/step - accuracy: 0.5149 - loss: 0.6928 - val_accuracy: 0.5009 - val_loss: 0.6932\n",
      "Epoch 25/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 3ms/step - accuracy: 0.5100 - loss: 0.6927 - val_accuracy: 0.4991 - val_loss: 0.6933\n",
      "Epoch 26/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5185 - loss: 0.6916 - val_accuracy: 0.4997 - val_loss: 0.6933\n",
      "Epoch 27/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5125 - loss: 0.6931 - val_accuracy: 0.4979 - val_loss: 0.6933\n",
      "Epoch 28/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5148 - loss: 0.6925 - val_accuracy: 0.4988 - val_loss: 0.6934\n",
      "Epoch 29/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 3ms/step - accuracy: 0.5128 - loss: 0.6922 - val_accuracy: 0.4994 - val_loss: 0.6934\n",
      "Epoch 30/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 3ms/step - accuracy: 0.5176 - loss: 0.6918 - val_accuracy: 0.5006 - val_loss: 0.6933\n",
      "Epoch 31/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 3ms/step - accuracy: 0.5184 - loss: 0.6919 - val_accuracy: 0.5018 - val_loss: 0.6934\n",
      "Epoch 32/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 3ms/step - accuracy: 0.5240 - loss: 0.6913 - val_accuracy: 0.5045 - val_loss: 0.6933\n",
      "Epoch 33/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5191 - loss: 0.6913 - val_accuracy: 0.5063 - val_loss: 0.6933\n",
      "Epoch 34/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5186 - loss: 0.6917 - val_accuracy: 0.5081 - val_loss: 0.6933\n",
      "Epoch 35/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5207 - loss: 0.6908 - val_accuracy: 0.5033 - val_loss: 0.6934\n",
      "Epoch 36/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5131 - loss: 0.6918 - val_accuracy: 0.5012 - val_loss: 0.6934\n",
      "Epoch 37/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 3ms/step - accuracy: 0.5210 - loss: 0.6922 - val_accuracy: 0.5048 - val_loss: 0.6933\n",
      "Epoch 38/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 3ms/step - accuracy: 0.5248 - loss: 0.6918 - val_accuracy: 0.5057 - val_loss: 0.6933\n",
      "Epoch 39/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 3ms/step - accuracy: 0.5238 - loss: 0.6912 - val_accuracy: 0.5027 - val_loss: 0.6934\n",
      "Epoch 40/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5303 - loss: 0.6915 - val_accuracy: 0.5027 - val_loss: 0.6933\n",
      "Epoch 41/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 3ms/step - accuracy: 0.5290 - loss: 0.6910 - val_accuracy: 0.5045 - val_loss: 0.6935\n",
      "Epoch 42/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5324 - loss: 0.6902 - val_accuracy: 0.5057 - val_loss: 0.6935\n",
      "Epoch 43/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5297 - loss: 0.6909 - val_accuracy: 0.5072 - val_loss: 0.6935\n",
      "Epoch 44/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 3ms/step - accuracy: 0.5208 - loss: 0.6923 - val_accuracy: 0.5060 - val_loss: 0.6935\n",
      "Epoch 45/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5160 - loss: 0.6917 - val_accuracy: 0.5048 - val_loss: 0.6934\n",
      "Epoch 46/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5218 - loss: 0.6915 - val_accuracy: 0.5066 - val_loss: 0.6934\n",
      "Epoch 47/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5330 - loss: 0.6908 - val_accuracy: 0.5087 - val_loss: 0.6935\n",
      "Epoch 48/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5216 - loss: 0.6916 - val_accuracy: 0.5081 - val_loss: 0.6936\n",
      "Epoch 49/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5360 - loss: 0.6894 - val_accuracy: 0.5045 - val_loss: 0.6937\n",
      "Epoch 50/50\n",
      "\u001B[1m418/418\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - accuracy: 0.5240 - loss: 0.6924 - val_accuracy: 0.5066 - val_loss: 0.6937\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x149a13d27d0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 29
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:55:19.668570Z",
     "start_time": "2024-08-24T00:55:19.239670Z"
    }
   },
   "cell_type": "code",
   "source": "model.evaluate(train_standardized[cols], train['dir'])",
   "id": "404ffe94258278f0",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1m522/522\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 689us/step - accuracy: 0.5431 - loss: 0.6897\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.6905357241630554, 0.5348433256149292]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 30
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:55:22.015143Z",
     "start_time": "2024-08-24T00:55:21.369818Z"
    }
   },
   "cell_type": "code",
   "source": [
    "predict = model.predict(train_standardized[cols])\n",
    "predict"
   ],
   "id": "7b01dd10c2f2c06f",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1m522/522\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 752us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.4899044 ],\n",
       "       [0.4851367 ],\n",
       "       [0.48515376],\n",
       "       ...,\n",
       "       [0.4929709 ],\n",
       "       [0.48289037],\n",
       "       [0.4797971 ]], dtype=float32)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 31
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:55:24.573723Z",
     "start_time": "2024-08-24T00:55:24.480094Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.hist(predict, bins=50)\n",
    "plt.show()"
   ],
   "id": "d5adf4a7861cfe7f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 32
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:55:27.207898Z",
     "start_time": "2024-08-24T00:55:27.179404Z"
    }
   },
   "cell_type": "code",
   "source": "test",
   "id": "143125c5ceaf1ed2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                     EUR_USD   returns  dir       sma      boll       min  \\\n",
       "time                                                                        \n",
       "2017-11-24 21:40:00  1.19350 -0.000369    0  0.003468  1.219148 -0.007993   \n",
       "2017-11-24 22:00:00  1.19325 -0.000209    0  0.003556  1.104014 -0.007785   \n",
       "2017-11-26 22:20:00  1.19320 -0.000042    0  0.003640  1.047758 -0.007744   \n",
       "2017-11-26 22:40:00  1.19307 -0.000109    0  0.003722  0.972170 -0.007636   \n",
       "2017-11-26 23:00:00  1.19354  0.000394    1  0.003810  1.062472 -0.008027   \n",
       "...                      ...       ...  ...       ...       ...       ...   \n",
       "2019-12-30 23:00:00  1.12012  0.000080    1  0.003163  0.116062 -0.001357   \n",
       "2019-12-30 23:20:00  1.12030  0.000161    1  0.003116  0.335572 -0.001517   \n",
       "2019-12-30 23:40:00  1.12018 -0.000107    0  0.003058  0.155421 -0.001410   \n",
       "2019-12-31 00:00:00  1.12013 -0.000045    0  0.002986  0.090286 -0.001366   \n",
       "2019-12-31 00:20:00  1.12030  0.000152    1  0.002913  0.327692 -0.001517   \n",
       "\n",
       "                          max           mom       vol  dir_lag_1  ...  \\\n",
       "time                                                              ...   \n",
       "2017-11-24 21:40:00  0.000394  2.290970e-04  0.000415        1.0  ...   \n",
       "2017-11-24 22:00:00  0.000603  4.469886e-05  0.000418        0.0  ...   \n",
       "2017-11-26 22:20:00  0.000645 -2.066629e-04  0.000418        0.0  ...   \n",
       "2017-11-26 22:40:00  0.000754 -1.201166e-04  0.000419        0.0  ...   \n",
       "2017-11-26 23:00:00  0.000360  8.100140e-05  0.000420        0.0  ...   \n",
       "...                       ...           ...       ...        ...  ...   \n",
       "2019-12-30 23:00:00  0.001446  5.952275e-05  0.000407        0.0  ...   \n",
       "2019-12-30 23:20:00  0.001285 -2.975261e-05  0.000396        1.0  ...   \n",
       "2019-12-30 23:40:00  0.001393  4.463867e-05  0.000395        1.0  ...   \n",
       "2019-12-31 00:00:00  0.001437  2.975858e-06  0.000385        0.0  ...   \n",
       "2019-12-31 00:20:00  0.001285 -2.684304e-17  0.000385        0.0  ...   \n",
       "\n",
       "                     mom_lag_1  mom_lag_2  mom_lag_3  mom_lag_4  mom_lag_5  \\\n",
       "time                                                                         \n",
       "2017-11-24 21:40:00   0.000355   0.000120   0.000042   0.000064   0.000034   \n",
       "2017-11-24 22:00:00   0.000229   0.000355   0.000120   0.000042   0.000064   \n",
       "2017-11-26 22:20:00   0.000045   0.000229   0.000355   0.000120   0.000042   \n",
       "2017-11-26 22:40:00  -0.000207   0.000045   0.000229   0.000355   0.000120   \n",
       "2017-11-26 23:00:00  -0.000120  -0.000207   0.000045   0.000229   0.000355   \n",
       "...                        ...        ...        ...        ...        ...   \n",
       "2019-12-30 23:00:00  -0.000068   0.000065   0.000021   0.000104   0.000098   \n",
       "2019-12-30 23:20:00   0.000060  -0.000068   0.000065   0.000021   0.000104   \n",
       "2019-12-30 23:40:00  -0.000030   0.000060  -0.000068   0.000065   0.000021   \n",
       "2019-12-31 00:00:00   0.000045  -0.000030   0.000060  -0.000068   0.000065   \n",
       "2019-12-31 00:20:00   0.000003   0.000045  -0.000030   0.000060  -0.000068   \n",
       "\n",
       "                     vol_lag_1  vol_lag_2  vol_lag_3  vol_lag_4  vol_lag_5  \n",
       "time                                                                        \n",
       "2017-11-24 21:40:00   0.000414   0.000407   0.000407   0.000412   0.000413  \n",
       "2017-11-24 22:00:00   0.000415   0.000414   0.000407   0.000407   0.000412  \n",
       "2017-11-26 22:20:00   0.000418   0.000415   0.000414   0.000407   0.000407  \n",
       "2017-11-26 22:40:00   0.000418   0.000418   0.000415   0.000414   0.000407  \n",
       "2017-11-26 23:00:00   0.000419   0.000418   0.000418   0.000415   0.000414  \n",
       "...                        ...        ...        ...        ...        ...  \n",
       "2019-12-30 23:00:00   0.000408   0.000406   0.000402   0.000401   0.000402  \n",
       "2019-12-30 23:20:00   0.000407   0.000408   0.000406   0.000402   0.000401  \n",
       "2019-12-30 23:40:00   0.000396   0.000407   0.000408   0.000406   0.000402  \n",
       "2019-12-31 00:00:00   0.000395   0.000396   0.000407   0.000408   0.000406  \n",
       "2019-12-31 00:20:00   0.000385   0.000395   0.000396   0.000407   0.000408  \n",
       "\n",
       "[38942 rows x 44 columns]"
      ],
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EUR_USD</th>\n",
       "      <th>returns</th>\n",
       "      <th>dir</th>\n",
       "      <th>sma</th>\n",
       "      <th>boll</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mom</th>\n",
       "      <th>vol</th>\n",
       "      <th>dir_lag_1</th>\n",
       "      <th>...</th>\n",
       "      <th>mom_lag_1</th>\n",
       "      <th>mom_lag_2</th>\n",
       "      <th>mom_lag_3</th>\n",
       "      <th>mom_lag_4</th>\n",
       "      <th>mom_lag_5</th>\n",
       "      <th>vol_lag_1</th>\n",
       "      <th>vol_lag_2</th>\n",
       "      <th>vol_lag_3</th>\n",
       "      <th>vol_lag_4</th>\n",
       "      <th>vol_lag_5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-24 21:40:00</th>\n",
       "      <td>1.19350</td>\n",
       "      <td>-0.000369</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003468</td>\n",
       "      <td>1.219148</td>\n",
       "      <td>-0.007993</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>2.290970e-04</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>0.000414</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>0.000413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24 22:00:00</th>\n",
       "      <td>1.19325</td>\n",
       "      <td>-0.000209</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003556</td>\n",
       "      <td>1.104014</td>\n",
       "      <td>-0.007785</td>\n",
       "      <td>0.000603</td>\n",
       "      <td>4.469886e-05</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000414</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-26 22:20:00</th>\n",
       "      <td>1.19320</td>\n",
       "      <td>-0.000042</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003640</td>\n",
       "      <td>1.047758</td>\n",
       "      <td>-0.007744</td>\n",
       "      <td>0.000645</td>\n",
       "      <td>-2.066629e-04</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000414</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-26 22:40:00</th>\n",
       "      <td>1.19307</td>\n",
       "      <td>-0.000109</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003722</td>\n",
       "      <td>0.972170</td>\n",
       "      <td>-0.007636</td>\n",
       "      <td>0.000754</td>\n",
       "      <td>-1.201166e-04</td>\n",
       "      <td>0.000419</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000207</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.000120</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000414</td>\n",
       "      <td>0.000407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-26 23:00:00</th>\n",
       "      <td>1.19354</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003810</td>\n",
       "      <td>1.062472</td>\n",
       "      <td>-0.008027</td>\n",
       "      <td>0.000360</td>\n",
       "      <td>8.100140e-05</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000120</td>\n",
       "      <td>-0.000207</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.000419</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.000415</td>\n",
       "      <td>0.000414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30 23:00:00</th>\n",
       "      <td>1.12012</td>\n",
       "      <td>0.000080</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003163</td>\n",
       "      <td>0.116062</td>\n",
       "      <td>-0.001357</td>\n",
       "      <td>0.001446</td>\n",
       "      <td>5.952275e-05</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000068</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.000098</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.000401</td>\n",
       "      <td>0.000402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30 23:20:00</th>\n",
       "      <td>1.12030</td>\n",
       "      <td>0.000161</td>\n",
       "      <td>1</td>\n",
       "      <td>0.003116</td>\n",
       "      <td>0.335572</td>\n",
       "      <td>-0.001517</td>\n",
       "      <td>0.001285</td>\n",
       "      <td>-2.975261e-05</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>-0.000068</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.000401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30 23:40:00</th>\n",
       "      <td>1.12018</td>\n",
       "      <td>-0.000107</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003058</td>\n",
       "      <td>0.155421</td>\n",
       "      <td>-0.001410</td>\n",
       "      <td>0.001393</td>\n",
       "      <td>4.463867e-05</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000030</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>-0.000068</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.000406</td>\n",
       "      <td>0.000402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 00:00:00</th>\n",
       "      <td>1.12013</td>\n",
       "      <td>-0.000045</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002986</td>\n",
       "      <td>0.090286</td>\n",
       "      <td>-0.001366</td>\n",
       "      <td>0.001437</td>\n",
       "      <td>2.975858e-06</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>-0.000030</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>-0.000068</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.000406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 00:20:00</th>\n",
       "      <td>1.12030</td>\n",
       "      <td>0.000152</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002913</td>\n",
       "      <td>0.327692</td>\n",
       "      <td>-0.001517</td>\n",
       "      <td>0.001285</td>\n",
       "      <td>-2.684304e-17</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>-0.000030</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>-0.000068</td>\n",
       "      <td>0.000385</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000408</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38942 rows × 44 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 33
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:55:29.394119Z",
     "start_time": "2024-08-24T00:55:29.351400Z"
    }
   },
   "cell_type": "code",
   "source": [
    "test_standardized = (test-mu) / std\n",
    "test_standardized"
   ],
   "id": "1140b538b329f78e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "                      EUR_USD   returns       dir       sma      boll  \\\n",
       "time                                                                    \n",
       "2017-11-24 21:40:00  1.376317 -0.691411 -0.992865  1.089976  0.808831   \n",
       "2017-11-24 22:00:00  1.371302 -0.398964 -0.992865  1.121765  0.728865   \n",
       "2017-11-26 22:20:00  1.370299 -0.090929 -0.992865  1.151881  0.689792   \n",
       "2017-11-26 22:40:00  1.367691 -0.214178 -0.992865  1.181040  0.637292   \n",
       "2017-11-26 23:00:00  1.377120  0.710040  1.007126  1.212853  0.700012   \n",
       "...                       ...       ...       ...       ...       ...   \n",
       "2019-12-30 23:00:00 -0.095738  0.133784  1.007126  0.980674  0.042680   \n",
       "2019-12-30 23:20:00 -0.092127  0.281440  1.007126  0.963895  0.195141   \n",
       "2019-12-30 23:40:00 -0.094534 -0.210802 -0.992865  0.943053  0.070017   \n",
       "2019-12-31 00:00:00 -0.095537 -0.095953 -0.992865  0.917096  0.024777   \n",
       "2019-12-31 00:20:00 -0.092127  0.265031  1.007126  0.890972  0.189668   \n",
       "\n",
       "                          min       max       mom       vol  dir_lag_1  ...  \\\n",
       "time                                                                    ...   \n",
       "2017-11-24 21:40:00 -2.063066 -0.881500  0.714557 -0.460111   1.007126  ...   \n",
       "2017-11-24 22:00:00 -1.983085 -0.781549  0.119802 -0.448317  -0.992865  ...   \n",
       "2017-11-26 22:20:00 -1.967085 -0.761554 -0.690936 -0.448703  -0.992865  ...   \n",
       "2017-11-26 22:40:00 -1.925477 -0.709558 -0.411791 -0.443430  -0.992865  ...   \n",
       "2017-11-26 23:00:00 -2.075860 -0.897488  0.236892 -0.436048  -0.992865  ...   \n",
       "...                       ...       ...       ...       ...        ...  ...   \n",
       "2019-12-30 23:00:00  0.490766 -0.379598  0.167615 -0.500448  -0.992865  ...   \n",
       "2019-12-30 23:20:00  0.429019 -0.456330 -0.120332 -0.556318   1.007126  ...   \n",
       "2019-12-30 23:40:00  0.470181 -0.405178  0.119608 -0.563294   1.007126  ...   \n",
       "2019-12-31 00:00:00  0.487335 -0.383862 -0.014770 -0.610573  -0.992865  ...   \n",
       "2019-12-31 00:20:00  0.429019 -0.456330 -0.024369 -0.609887  -0.992865  ...   \n",
       "\n",
       "                     mom_lag_1  mom_lag_2  mom_lag_3  mom_lag_4  mom_lag_5  \\\n",
       "time                                                                         \n",
       "2017-11-24 21:40:00   1.119968   0.363368   0.111120   0.183271   0.083801   \n",
       "2017-11-24 22:00:00   0.714650   1.119966   0.363420   0.111152   0.182902   \n",
       "2017-11-26 22:20:00   0.119873   0.714704   1.120000   0.363451   0.110826   \n",
       "2017-11-26 22:40:00  -0.690896   0.120008   0.714748   1.120029   0.362976   \n",
       "2017-11-26 23:00:00  -0.411740  -0.690650   0.120066   0.714778   1.119106   \n",
       "...                        ...        ...        ...        ...        ...   \n",
       "2019-12-30 23:00:00  -0.245069   0.186962   0.043107   0.311855   0.292246   \n",
       "2019-12-30 23:20:00   0.167687  -0.244883   0.187018   0.043139   0.311410   \n",
       "2019-12-30 23:40:00  -0.120271   0.167816  -0.244817   0.187050   0.042852   \n",
       "2019-12-31 00:00:00   0.119679  -0.120103   0.167873  -0.244784   0.186678   \n",
       "2019-12-31 00:20:00  -0.014705   0.119814  -0.120039   0.167904  -0.244900   \n",
       "\n",
       "                     vol_lag_1  vol_lag_2  vol_lag_3  vol_lag_4  vol_lag_5  \n",
       "time                                                                        \n",
       "2017-11-24 21:40:00  -0.464958  -0.500855  -0.502496  -0.475399  -0.473006  \n",
       "2017-11-24 22:00:00  -0.460204  -0.465053  -0.500947  -0.502586  -0.475488  \n",
       "2017-11-26 22:20:00  -0.448411  -0.460300  -0.465148  -0.501037  -0.502674  \n",
       "2017-11-26 22:40:00  -0.448797  -0.448507  -0.460395  -0.465241  -0.501125  \n",
       "2017-11-26 23:00:00  -0.443524  -0.448893  -0.448604  -0.460488  -0.465331  \n",
       "...                        ...        ...        ...        ...        ...  \n",
       "2019-12-30 23:00:00  -0.494708  -0.506245  -0.524932  -0.531570  -0.526876  \n",
       "2019-12-30 23:20:00  -0.500538  -0.494801  -0.506337  -0.525020  -0.531655  \n",
       "2019-12-30 23:40:00  -0.556403  -0.500630  -0.494894  -0.506427  -0.525106  \n",
       "2019-12-31 00:00:00  -0.563378  -0.556491  -0.500722  -0.494984  -0.506514  \n",
       "2019-12-31 00:20:00  -0.610654  -0.563466  -0.556579  -0.500813  -0.495072  \n",
       "\n",
       "[38942 rows x 44 columns]"
      ],
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EUR_USD</th>\n",
       "      <th>returns</th>\n",
       "      <th>dir</th>\n",
       "      <th>sma</th>\n",
       "      <th>boll</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mom</th>\n",
       "      <th>vol</th>\n",
       "      <th>dir_lag_1</th>\n",
       "      <th>...</th>\n",
       "      <th>mom_lag_1</th>\n",
       "      <th>mom_lag_2</th>\n",
       "      <th>mom_lag_3</th>\n",
       "      <th>mom_lag_4</th>\n",
       "      <th>mom_lag_5</th>\n",
       "      <th>vol_lag_1</th>\n",
       "      <th>vol_lag_2</th>\n",
       "      <th>vol_lag_3</th>\n",
       "      <th>vol_lag_4</th>\n",
       "      <th>vol_lag_5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-24 21:40:00</th>\n",
       "      <td>1.376317</td>\n",
       "      <td>-0.691411</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>1.089976</td>\n",
       "      <td>0.808831</td>\n",
       "      <td>-2.063066</td>\n",
       "      <td>-0.881500</td>\n",
       "      <td>0.714557</td>\n",
       "      <td>-0.460111</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>...</td>\n",
       "      <td>1.119968</td>\n",
       "      <td>0.363368</td>\n",
       "      <td>0.111120</td>\n",
       "      <td>0.183271</td>\n",
       "      <td>0.083801</td>\n",
       "      <td>-0.464958</td>\n",
       "      <td>-0.500855</td>\n",
       "      <td>-0.502496</td>\n",
       "      <td>-0.475399</td>\n",
       "      <td>-0.473006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-24 22:00:00</th>\n",
       "      <td>1.371302</td>\n",
       "      <td>-0.398964</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>1.121765</td>\n",
       "      <td>0.728865</td>\n",
       "      <td>-1.983085</td>\n",
       "      <td>-0.781549</td>\n",
       "      <td>0.119802</td>\n",
       "      <td>-0.448317</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>...</td>\n",
       "      <td>0.714650</td>\n",
       "      <td>1.119966</td>\n",
       "      <td>0.363420</td>\n",
       "      <td>0.111152</td>\n",
       "      <td>0.182902</td>\n",
       "      <td>-0.460204</td>\n",
       "      <td>-0.465053</td>\n",
       "      <td>-0.500947</td>\n",
       "      <td>-0.502586</td>\n",
       "      <td>-0.475488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-26 22:20:00</th>\n",
       "      <td>1.370299</td>\n",
       "      <td>-0.090929</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>1.151881</td>\n",
       "      <td>0.689792</td>\n",
       "      <td>-1.967085</td>\n",
       "      <td>-0.761554</td>\n",
       "      <td>-0.690936</td>\n",
       "      <td>-0.448703</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>...</td>\n",
       "      <td>0.119873</td>\n",
       "      <td>0.714704</td>\n",
       "      <td>1.120000</td>\n",
       "      <td>0.363451</td>\n",
       "      <td>0.110826</td>\n",
       "      <td>-0.448411</td>\n",
       "      <td>-0.460300</td>\n",
       "      <td>-0.465148</td>\n",
       "      <td>-0.501037</td>\n",
       "      <td>-0.502674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-26 22:40:00</th>\n",
       "      <td>1.367691</td>\n",
       "      <td>-0.214178</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>1.181040</td>\n",
       "      <td>0.637292</td>\n",
       "      <td>-1.925477</td>\n",
       "      <td>-0.709558</td>\n",
       "      <td>-0.411791</td>\n",
       "      <td>-0.443430</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.690896</td>\n",
       "      <td>0.120008</td>\n",
       "      <td>0.714748</td>\n",
       "      <td>1.120029</td>\n",
       "      <td>0.362976</td>\n",
       "      <td>-0.448797</td>\n",
       "      <td>-0.448507</td>\n",
       "      <td>-0.460395</td>\n",
       "      <td>-0.465241</td>\n",
       "      <td>-0.501125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-26 23:00:00</th>\n",
       "      <td>1.377120</td>\n",
       "      <td>0.710040</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>1.212853</td>\n",
       "      <td>0.700012</td>\n",
       "      <td>-2.075860</td>\n",
       "      <td>-0.897488</td>\n",
       "      <td>0.236892</td>\n",
       "      <td>-0.436048</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.411740</td>\n",
       "      <td>-0.690650</td>\n",
       "      <td>0.120066</td>\n",
       "      <td>0.714778</td>\n",
       "      <td>1.119106</td>\n",
       "      <td>-0.443524</td>\n",
       "      <td>-0.448893</td>\n",
       "      <td>-0.448604</td>\n",
       "      <td>-0.460488</td>\n",
       "      <td>-0.465331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30 23:00:00</th>\n",
       "      <td>-0.095738</td>\n",
       "      <td>0.133784</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>0.980674</td>\n",
       "      <td>0.042680</td>\n",
       "      <td>0.490766</td>\n",
       "      <td>-0.379598</td>\n",
       "      <td>0.167615</td>\n",
       "      <td>-0.500448</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.245069</td>\n",
       "      <td>0.186962</td>\n",
       "      <td>0.043107</td>\n",
       "      <td>0.311855</td>\n",
       "      <td>0.292246</td>\n",
       "      <td>-0.494708</td>\n",
       "      <td>-0.506245</td>\n",
       "      <td>-0.524932</td>\n",
       "      <td>-0.531570</td>\n",
       "      <td>-0.526876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30 23:20:00</th>\n",
       "      <td>-0.092127</td>\n",
       "      <td>0.281440</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>0.963895</td>\n",
       "      <td>0.195141</td>\n",
       "      <td>0.429019</td>\n",
       "      <td>-0.456330</td>\n",
       "      <td>-0.120332</td>\n",
       "      <td>-0.556318</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>...</td>\n",
       "      <td>0.167687</td>\n",
       "      <td>-0.244883</td>\n",
       "      <td>0.187018</td>\n",
       "      <td>0.043139</td>\n",
       "      <td>0.311410</td>\n",
       "      <td>-0.500538</td>\n",
       "      <td>-0.494801</td>\n",
       "      <td>-0.506337</td>\n",
       "      <td>-0.525020</td>\n",
       "      <td>-0.531655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30 23:40:00</th>\n",
       "      <td>-0.094534</td>\n",
       "      <td>-0.210802</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>0.943053</td>\n",
       "      <td>0.070017</td>\n",
       "      <td>0.470181</td>\n",
       "      <td>-0.405178</td>\n",
       "      <td>0.119608</td>\n",
       "      <td>-0.563294</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.120271</td>\n",
       "      <td>0.167816</td>\n",
       "      <td>-0.244817</td>\n",
       "      <td>0.187050</td>\n",
       "      <td>0.042852</td>\n",
       "      <td>-0.556403</td>\n",
       "      <td>-0.500630</td>\n",
       "      <td>-0.494894</td>\n",
       "      <td>-0.506427</td>\n",
       "      <td>-0.525106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 00:00:00</th>\n",
       "      <td>-0.095537</td>\n",
       "      <td>-0.095953</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>0.917096</td>\n",
       "      <td>0.024777</td>\n",
       "      <td>0.487335</td>\n",
       "      <td>-0.383862</td>\n",
       "      <td>-0.014770</td>\n",
       "      <td>-0.610573</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>...</td>\n",
       "      <td>0.119679</td>\n",
       "      <td>-0.120103</td>\n",
       "      <td>0.167873</td>\n",
       "      <td>-0.244784</td>\n",
       "      <td>0.186678</td>\n",
       "      <td>-0.563378</td>\n",
       "      <td>-0.556491</td>\n",
       "      <td>-0.500722</td>\n",
       "      <td>-0.494984</td>\n",
       "      <td>-0.506514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31 00:20:00</th>\n",
       "      <td>-0.092127</td>\n",
       "      <td>0.265031</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>0.890972</td>\n",
       "      <td>0.189668</td>\n",
       "      <td>0.429019</td>\n",
       "      <td>-0.456330</td>\n",
       "      <td>-0.024369</td>\n",
       "      <td>-0.609887</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.014705</td>\n",
       "      <td>0.119814</td>\n",
       "      <td>-0.120039</td>\n",
       "      <td>0.167904</td>\n",
       "      <td>-0.244900</td>\n",
       "      <td>-0.610654</td>\n",
       "      <td>-0.563466</td>\n",
       "      <td>-0.556579</td>\n",
       "      <td>-0.500813</td>\n",
       "      <td>-0.495072</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38942 rows × 44 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 34
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:55:31.084186Z",
     "start_time": "2024-08-24T00:55:31.001618Z"
    }
   },
   "cell_type": "code",
   "source": "test_standardized.describe()",
   "id": "395aa81cb155fad2",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            EUR_USD       returns           dir           sma          boll  \\\n",
       "count  38942.000000  38942.000000  38942.000000  38942.000000  38942.000000   \n",
       "mean       0.543969     -0.016913     -0.012180     -0.184151     -0.052805   \n",
       "std        0.826861      0.869117      0.999822      0.887934      0.996951   \n",
       "min       -0.738885    -12.171228     -0.992865     -4.766964     -4.035323   \n",
       "25%       -0.087513     -0.394806     -0.992865     -0.749698     -0.768976   \n",
       "50%        0.284212     -0.013908     -0.992865     -0.192590     -0.051949   \n",
       "75%        0.997170      0.363170      1.007126      0.375752      0.681084   \n",
       "max        2.608848     10.996107      1.007126      3.397163      4.117524   \n",
       "\n",
       "                min           max           mom           vol     dir_lag_1  \\\n",
       "count  38942.000000  38942.000000  38942.000000  38942.000000  38942.000000   \n",
       "mean       0.209895     -0.044757     -0.029600     -0.326891     -0.012180   \n",
       "std        0.765278      0.999591      0.869203      0.847141      0.999822   \n",
       "min       -4.588884     -1.069294     -9.308050     -2.127288     -0.992865   \n",
       "25%       -0.135547     -0.767658     -0.401980     -0.942800     -0.992865   \n",
       "50%        0.440950     -0.328185     -0.024369     -0.444913     -0.992865   \n",
       "75%        0.769441      0.395308      0.345286      0.114874      1.007126   \n",
       "max        1.012976      9.937612      8.701290      4.422633      1.007126   \n",
       "\n",
       "       ...     mom_lag_1     mom_lag_2     mom_lag_3     mom_lag_4  \\\n",
       "count  ...  38942.000000  38942.000000  38942.000000  38942.000000   \n",
       "mean   ...     -0.029506     -0.029340     -0.029279     -0.029239   \n",
       "std    ...      0.869255      0.869139      0.869118      0.869116   \n",
       "min    ...     -9.308334     -9.306908     -9.306623     -9.306566   \n",
       "25%    ...     -0.401929     -0.401722     -0.401652     -0.401619   \n",
       "50%    ...     -0.024304     -0.024149     -0.024087     -0.024055   \n",
       "75%    ...      0.345429      0.345549      0.345601      0.345632   \n",
       "max    ...      8.701683      8.700644      8.700495      8.700504   \n",
       "\n",
       "          mom_lag_5     vol_lag_1     vol_lag_2     vol_lag_3     vol_lag_4  \\\n",
       "count  38942.000000  38942.000000  38942.000000  38942.000000  38942.000000   \n",
       "mean      -0.029485     -0.326991     -0.327094     -0.327199     -0.327300   \n",
       "std        0.868602      0.847072      0.847005      0.846937      0.846872   \n",
       "min       -9.301332     -2.127248     -2.127212     -2.127175     -2.127140   \n",
       "25%       -0.401643     -0.942854     -0.942912     -0.942969     -0.943025   \n",
       "50%       -0.024302     -0.445007     -0.445104     -0.445201     -0.445295   \n",
       "75%        0.345167      0.114735      0.114594      0.114453      0.114316   \n",
       "max        8.695106      4.422151      4.421671      4.421187      4.420720   \n",
       "\n",
       "          vol_lag_5  \n",
       "count  38942.000000  \n",
       "mean      -0.327397  \n",
       "std        0.846822  \n",
       "min       -2.127132  \n",
       "25%       -0.943087  \n",
       "50%       -0.445386  \n",
       "75%        0.114192  \n",
       "max        4.420343  \n",
       "\n",
       "[8 rows x 44 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EUR_USD</th>\n",
       "      <th>returns</th>\n",
       "      <th>dir</th>\n",
       "      <th>sma</th>\n",
       "      <th>boll</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mom</th>\n",
       "      <th>vol</th>\n",
       "      <th>dir_lag_1</th>\n",
       "      <th>...</th>\n",
       "      <th>mom_lag_1</th>\n",
       "      <th>mom_lag_2</th>\n",
       "      <th>mom_lag_3</th>\n",
       "      <th>mom_lag_4</th>\n",
       "      <th>mom_lag_5</th>\n",
       "      <th>vol_lag_1</th>\n",
       "      <th>vol_lag_2</th>\n",
       "      <th>vol_lag_3</th>\n",
       "      <th>vol_lag_4</th>\n",
       "      <th>vol_lag_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>38942.000000</td>\n",
       "      <td>38942.000000</td>\n",
       "      <td>38942.000000</td>\n",
       "      <td>38942.000000</td>\n",
       "      <td>38942.000000</td>\n",
       "      <td>38942.000000</td>\n",
       "      <td>38942.000000</td>\n",
       "      <td>38942.000000</td>\n",
       "      <td>38942.000000</td>\n",
       "      <td>38942.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>38942.000000</td>\n",
       "      <td>38942.000000</td>\n",
       "      <td>38942.000000</td>\n",
       "      <td>38942.000000</td>\n",
       "      <td>38942.000000</td>\n",
       "      <td>38942.000000</td>\n",
       "      <td>38942.000000</td>\n",
       "      <td>38942.000000</td>\n",
       "      <td>38942.000000</td>\n",
       "      <td>38942.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.543969</td>\n",
       "      <td>-0.016913</td>\n",
       "      <td>-0.012180</td>\n",
       "      <td>-0.184151</td>\n",
       "      <td>-0.052805</td>\n",
       "      <td>0.209895</td>\n",
       "      <td>-0.044757</td>\n",
       "      <td>-0.029600</td>\n",
       "      <td>-0.326891</td>\n",
       "      <td>-0.012180</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.029506</td>\n",
       "      <td>-0.029340</td>\n",
       "      <td>-0.029279</td>\n",
       "      <td>-0.029239</td>\n",
       "      <td>-0.029485</td>\n",
       "      <td>-0.326991</td>\n",
       "      <td>-0.327094</td>\n",
       "      <td>-0.327199</td>\n",
       "      <td>-0.327300</td>\n",
       "      <td>-0.327397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.826861</td>\n",
       "      <td>0.869117</td>\n",
       "      <td>0.999822</td>\n",
       "      <td>0.887934</td>\n",
       "      <td>0.996951</td>\n",
       "      <td>0.765278</td>\n",
       "      <td>0.999591</td>\n",
       "      <td>0.869203</td>\n",
       "      <td>0.847141</td>\n",
       "      <td>0.999822</td>\n",
       "      <td>...</td>\n",
       "      <td>0.869255</td>\n",
       "      <td>0.869139</td>\n",
       "      <td>0.869118</td>\n",
       "      <td>0.869116</td>\n",
       "      <td>0.868602</td>\n",
       "      <td>0.847072</td>\n",
       "      <td>0.847005</td>\n",
       "      <td>0.846937</td>\n",
       "      <td>0.846872</td>\n",
       "      <td>0.846822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.738885</td>\n",
       "      <td>-12.171228</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>-4.766964</td>\n",
       "      <td>-4.035323</td>\n",
       "      <td>-4.588884</td>\n",
       "      <td>-1.069294</td>\n",
       "      <td>-9.308050</td>\n",
       "      <td>-2.127288</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>...</td>\n",
       "      <td>-9.308334</td>\n",
       "      <td>-9.306908</td>\n",
       "      <td>-9.306623</td>\n",
       "      <td>-9.306566</td>\n",
       "      <td>-9.301332</td>\n",
       "      <td>-2.127248</td>\n",
       "      <td>-2.127212</td>\n",
       "      <td>-2.127175</td>\n",
       "      <td>-2.127140</td>\n",
       "      <td>-2.127132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.087513</td>\n",
       "      <td>-0.394806</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>-0.749698</td>\n",
       "      <td>-0.768976</td>\n",
       "      <td>-0.135547</td>\n",
       "      <td>-0.767658</td>\n",
       "      <td>-0.401980</td>\n",
       "      <td>-0.942800</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.401929</td>\n",
       "      <td>-0.401722</td>\n",
       "      <td>-0.401652</td>\n",
       "      <td>-0.401619</td>\n",
       "      <td>-0.401643</td>\n",
       "      <td>-0.942854</td>\n",
       "      <td>-0.942912</td>\n",
       "      <td>-0.942969</td>\n",
       "      <td>-0.943025</td>\n",
       "      <td>-0.943087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.284212</td>\n",
       "      <td>-0.013908</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>-0.192590</td>\n",
       "      <td>-0.051949</td>\n",
       "      <td>0.440950</td>\n",
       "      <td>-0.328185</td>\n",
       "      <td>-0.024369</td>\n",
       "      <td>-0.444913</td>\n",
       "      <td>-0.992865</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.024304</td>\n",
       "      <td>-0.024149</td>\n",
       "      <td>-0.024087</td>\n",
       "      <td>-0.024055</td>\n",
       "      <td>-0.024302</td>\n",
       "      <td>-0.445007</td>\n",
       "      <td>-0.445104</td>\n",
       "      <td>-0.445201</td>\n",
       "      <td>-0.445295</td>\n",
       "      <td>-0.445386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.997170</td>\n",
       "      <td>0.363170</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>0.375752</td>\n",
       "      <td>0.681084</td>\n",
       "      <td>0.769441</td>\n",
       "      <td>0.395308</td>\n",
       "      <td>0.345286</td>\n",
       "      <td>0.114874</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>...</td>\n",
       "      <td>0.345429</td>\n",
       "      <td>0.345549</td>\n",
       "      <td>0.345601</td>\n",
       "      <td>0.345632</td>\n",
       "      <td>0.345167</td>\n",
       "      <td>0.114735</td>\n",
       "      <td>0.114594</td>\n",
       "      <td>0.114453</td>\n",
       "      <td>0.114316</td>\n",
       "      <td>0.114192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.608848</td>\n",
       "      <td>10.996107</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>3.397163</td>\n",
       "      <td>4.117524</td>\n",
       "      <td>1.012976</td>\n",
       "      <td>9.937612</td>\n",
       "      <td>8.701290</td>\n",
       "      <td>4.422633</td>\n",
       "      <td>1.007126</td>\n",
       "      <td>...</td>\n",
       "      <td>8.701683</td>\n",
       "      <td>8.700644</td>\n",
       "      <td>8.700495</td>\n",
       "      <td>8.700504</td>\n",
       "      <td>8.695106</td>\n",
       "      <td>4.422151</td>\n",
       "      <td>4.421671</td>\n",
       "      <td>4.421187</td>\n",
       "      <td>4.420720</td>\n",
       "      <td>4.420343</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 44 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 35
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:55:33.625746Z",
     "start_time": "2024-08-24T00:55:32.819798Z"
    }
   },
   "cell_type": "code",
   "source": "model.evaluate(test_standardized[cols], test['dir'])",
   "id": "a1412bdb5e031958",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1m1217/1217\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 598us/step - accuracy: 0.5152 - loss: 0.6920\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.6920503377914429, 0.5179497599601746]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 36
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:55:35.667447Z",
     "start_time": "2024-08-24T00:55:34.599904Z"
    }
   },
   "cell_type": "code",
   "source": [
    "pred = model.predict(test_standardized[cols])\n",
    "pred"
   ],
   "id": "cdd722a977462451",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1m1217/1217\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 628us/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0.47563934],\n",
       "       [0.4803499 ],\n",
       "       [0.47799626],\n",
       "       ...,\n",
       "       [0.47691375],\n",
       "       [0.48948404],\n",
       "       [0.48921296]], dtype=float32)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 37
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:55:38.314479Z",
     "start_time": "2024-08-24T00:55:37.399804Z"
    }
   },
   "cell_type": "code",
   "source": "test['probability'] = model.predict(test_standardized[cols])",
   "id": "c2986b7ad6937762",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1m1217/1217\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 556us/step\n"
     ]
    }
   ],
   "execution_count": 38
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:55:40.025412Z",
     "start_time": "2024-08-24T00:55:39.899516Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.hist(test['probability'], bins=100)\n",
    "plt.show()"
   ],
   "id": "b8a3b2af5a144d2f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 39
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:55:48.204270Z",
     "start_time": "2024-08-24T00:55:48.098440Z"
    }
   },
   "cell_type": "code",
   "source": [
    "plt.hist(pred, bins=100)\n",
    "plt.show()"
   ],
   "id": "15f72a22aff2686b",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 40
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:55:51.375647Z",
     "start_time": "2024-08-24T00:55:51.370105Z"
    }
   },
   "cell_type": "code",
   "source": "threshold = 0.55",
   "id": "78069ba49b55c070",
   "outputs": [],
   "execution_count": 41
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:55:52.899575Z",
     "start_time": "2024-08-24T00:55:52.889988Z"
    }
   },
   "cell_type": "code",
   "source": [
    "test['position'] = np.where(test.probability < (1-threshold), -1, np.nan)\n",
    "test['position'] = np.where(test.probability > threshold, 1, test.position)"
   ],
   "id": "8083116bed1fe173",
   "outputs": [],
   "execution_count": 42
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:55:54.279668Z",
     "start_time": "2024-08-24T00:55:54.269983Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pytz\n",
    "\n",
    "for code, name in pytz.country_names.items():\n",
    "    print(code, ':', name)"
   ],
   "id": "282993e8ffdbb82a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AD : Andorra\n",
      "AE : United Arab Emirates\n",
      "AF : Afghanistan\n",
      "AG : Antigua & Barbuda\n",
      "AI : Anguilla\n",
      "AL : Albania\n",
      "AM : Armenia\n",
      "AO : Angola\n",
      "AQ : Antarctica\n",
      "AR : Argentina\n",
      "AS : Samoa (American)\n",
      "AT : Austria\n",
      "AU : Australia\n",
      "AW : Aruba\n",
      "AX : Åland Islands\n",
      "AZ : Azerbaijan\n",
      "BA : Bosnia & Herzegovina\n",
      "BB : Barbados\n",
      "BD : Bangladesh\n",
      "BE : Belgium\n",
      "BF : Burkina Faso\n",
      "BG : Bulgaria\n",
      "BH : Bahrain\n",
      "BI : Burundi\n",
      "BJ : Benin\n",
      "BL : St Barthelemy\n",
      "BM : Bermuda\n",
      "BN : Brunei\n",
      "BO : Bolivia\n",
      "BQ : Caribbean NL\n",
      "BR : Brazil\n",
      "BS : Bahamas\n",
      "BT : Bhutan\n",
      "BV : Bouvet Island\n",
      "BW : Botswana\n",
      "BY : Belarus\n",
      "BZ : Belize\n",
      "CA : Canada\n",
      "CC : Cocos (Keeling) Islands\n",
      "CD : Congo (Dem. Rep.)\n",
      "CF : Central African Rep.\n",
      "CG : Congo (Rep.)\n",
      "CH : Switzerland\n",
      "CI : Côte d'Ivoire\n",
      "CK : Cook Islands\n",
      "CL : Chile\n",
      "CM : Cameroon\n",
      "CN : China\n",
      "CO : Colombia\n",
      "CR : Costa Rica\n",
      "CU : Cuba\n",
      "CV : Cape Verde\n",
      "CW : Curaçao\n",
      "CX : Christmas Island\n",
      "CY : Cyprus\n",
      "CZ : Czech Republic\n",
      "DE : Germany\n",
      "DJ : Djibouti\n",
      "DK : Denmark\n",
      "DM : Dominica\n",
      "DO : Dominican Republic\n",
      "DZ : Algeria\n",
      "EC : Ecuador\n",
      "EE : Estonia\n",
      "EG : Egypt\n",
      "EH : Western Sahara\n",
      "ER : Eritrea\n",
      "ES : Spain\n",
      "ET : Ethiopia\n",
      "FI : Finland\n",
      "FJ : Fiji\n",
      "FK : Falkland Islands\n",
      "FM : Micronesia\n",
      "FO : Faroe Islands\n",
      "FR : France\n",
      "GA : Gabon\n",
      "GB : Britain (UK)\n",
      "GD : Grenada\n",
      "GE : Georgia\n",
      "GF : French Guiana\n",
      "GG : Guernsey\n",
      "GH : Ghana\n",
      "GI : Gibraltar\n",
      "GL : Greenland\n",
      "GM : Gambia\n",
      "GN : Guinea\n",
      "GP : Guadeloupe\n",
      "GQ : Equatorial Guinea\n",
      "GR : Greece\n",
      "GS : South Georgia & the South Sandwich Islands\n",
      "GT : Guatemala\n",
      "GU : Guam\n",
      "GW : Guinea-Bissau\n",
      "GY : Guyana\n",
      "HK : Hong Kong\n",
      "HM : Heard Island & McDonald Islands\n",
      "HN : Honduras\n",
      "HR : Croatia\n",
      "HT : Haiti\n",
      "HU : Hungary\n",
      "ID : Indonesia\n",
      "IE : Ireland\n",
      "IL : Israel\n",
      "IM : Isle of Man\n",
      "IN : India\n",
      "IO : British Indian Ocean Territory\n",
      "IQ : Iraq\n",
      "IR : Iran\n",
      "IS : Iceland\n",
      "IT : Italy\n",
      "JE : Jersey\n",
      "JM : Jamaica\n",
      "JO : Jordan\n",
      "JP : Japan\n",
      "KE : Kenya\n",
      "KG : Kyrgyzstan\n",
      "KH : Cambodia\n",
      "KI : Kiribati\n",
      "KM : Comoros\n",
      "KN : St Kitts & Nevis\n",
      "KP : Korea (North)\n",
      "KR : Korea (South)\n",
      "KW : Kuwait\n",
      "KY : Cayman Islands\n",
      "KZ : Kazakhstan\n",
      "LA : Laos\n",
      "LB : Lebanon\n",
      "LC : St Lucia\n",
      "LI : Liechtenstein\n",
      "LK : Sri Lanka\n",
      "LR : Liberia\n",
      "LS : Lesotho\n",
      "LT : Lithuania\n",
      "LU : Luxembourg\n",
      "LV : Latvia\n",
      "LY : Libya\n",
      "MA : Morocco\n",
      "MC : Monaco\n",
      "MD : Moldova\n",
      "ME : Montenegro\n",
      "MF : St Martin (French)\n",
      "MG : Madagascar\n",
      "MH : Marshall Islands\n",
      "MK : North Macedonia\n",
      "ML : Mali\n",
      "MM : Myanmar (Burma)\n",
      "MN : Mongolia\n",
      "MO : Macau\n",
      "MP : Northern Mariana Islands\n",
      "MQ : Martinique\n",
      "MR : Mauritania\n",
      "MS : Montserrat\n",
      "MT : Malta\n",
      "MU : Mauritius\n",
      "MV : Maldives\n",
      "MW : Malawi\n",
      "MX : Mexico\n",
      "MY : Malaysia\n",
      "MZ : Mozambique\n",
      "NA : Namibia\n",
      "NC : New Caledonia\n",
      "NE : Niger\n",
      "NF : Norfolk Island\n",
      "NG : Nigeria\n",
      "NI : Nicaragua\n",
      "NL : Netherlands\n",
      "NO : Norway\n",
      "NP : Nepal\n",
      "NR : Nauru\n",
      "NU : Niue\n",
      "NZ : New Zealand\n",
      "OM : Oman\n",
      "PA : Panama\n",
      "PE : Peru\n",
      "PF : French Polynesia\n",
      "PG : Papua New Guinea\n",
      "PH : Philippines\n",
      "PK : Pakistan\n",
      "PL : Poland\n",
      "PM : St Pierre & Miquelon\n",
      "PN : Pitcairn\n",
      "PR : Puerto Rico\n",
      "PS : Palestine\n",
      "PT : Portugal\n",
      "PW : Palau\n",
      "PY : Paraguay\n",
      "QA : Qatar\n",
      "RE : Réunion\n",
      "RO : Romania\n",
      "RS : Serbia\n",
      "RU : Russia\n",
      "RW : Rwanda\n",
      "SA : Saudi Arabia\n",
      "SB : Solomon Islands\n",
      "SC : Seychelles\n",
      "SD : Sudan\n",
      "SE : Sweden\n",
      "SG : Singapore\n",
      "SH : St Helena\n",
      "SI : Slovenia\n",
      "SJ : Svalbard & Jan Mayen\n",
      "SK : Slovakia\n",
      "SL : Sierra Leone\n",
      "SM : San Marino\n",
      "SN : Senegal\n",
      "SO : Somalia\n",
      "SR : Suriname\n",
      "SS : South Sudan\n",
      "ST : Sao Tome & Principe\n",
      "SV : El Salvador\n",
      "SX : St Maarten (Dutch)\n",
      "SY : Syria\n",
      "SZ : Eswatini (Swaziland)\n",
      "TC : Turks & Caicos Is\n",
      "TD : Chad\n",
      "TF : French S. Terr.\n",
      "TG : Togo\n",
      "TH : Thailand\n",
      "TJ : Tajikistan\n",
      "TK : Tokelau\n",
      "TL : East Timor\n",
      "TM : Turkmenistan\n",
      "TN : Tunisia\n",
      "TO : Tonga\n",
      "TR : Turkey\n",
      "TT : Trinidad & Tobago\n",
      "TV : Tuvalu\n",
      "TW : Taiwan\n",
      "TZ : Tanzania\n",
      "UA : Ukraine\n",
      "UG : Uganda\n",
      "UM : US minor outlying islands\n",
      "US : United States\n",
      "UY : Uruguay\n",
      "UZ : Uzbekistan\n",
      "VA : Vatican City\n",
      "VC : St Vincent\n",
      "VE : Venezuela\n",
      "VG : Virgin Islands (UK)\n",
      "VI : Virgin Islands (US)\n",
      "VN : Vietnam\n",
      "VU : Vanuatu\n",
      "WF : Wallis & Futuna\n",
      "WS : Samoa (western)\n",
      "YE : Yemen\n",
      "YT : Mayotte\n",
      "ZA : South Africa\n",
      "ZM : Zambia\n",
      "ZW : Zimbabwe\n"
     ]
    }
   ],
   "execution_count": 43
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:56:10.741696Z",
     "start_time": "2024-08-24T00:56:10.729821Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#test.index = test.index.tz_localize('utc')\n",
    "test['NYTime'] = test.index.tz_convert('America/New_York')\n",
    "test['hour'] = test.NYTime.dt.hour"
   ],
   "id": "467864f11d53f46e",
   "outputs": [],
   "execution_count": 46
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:56:12.403829Z",
     "start_time": "2024-08-24T00:56:12.398380Z"
    }
   },
   "cell_type": "code",
   "source": "test['position'] = np.where(~test.hour.between(2,12), 0, test.position)",
   "id": "bf4aa9860b079016",
   "outputs": [],
   "execution_count": 47
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:56:14.403604Z",
     "start_time": "2024-08-24T00:56:14.395638Z"
    }
   },
   "cell_type": "code",
   "source": "test.position.value_counts(dropna=False)",
   "id": "cc05fac222fc8c8f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "position\n",
       " 0.0    21089\n",
       " NaN    17379\n",
       "-1.0      346\n",
       " 1.0      128\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 48
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:56:16.302375Z",
     "start_time": "2024-08-24T00:56:16.297516Z"
    }
   },
   "cell_type": "code",
   "source": "test['position'] = test.position.ffill()",
   "id": "a55af2faef6fb93d",
   "outputs": [],
   "execution_count": 49
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:56:17.219722Z",
     "start_time": "2024-08-24T00:56:17.209802Z"
    }
   },
   "cell_type": "code",
   "source": "test.position.value_counts(dropna=False)",
   "id": "2fe86aaf424780a1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "position\n",
       " 0.0    33840\n",
       "-1.0     3773\n",
       " 1.0     1329\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 50
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:56:18.716185Z",
     "start_time": "2024-08-24T00:56:18.710391Z"
    }
   },
   "cell_type": "code",
   "source": "test['strategy'] = test['position'] * test['returns']",
   "id": "61adc364816d9c68",
   "outputs": [],
   "execution_count": 51
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:56:20.181331Z",
     "start_time": "2024-08-24T00:56:20.172557Z"
    }
   },
   "cell_type": "code",
   "source": [
    "test['creturns'] = test['returns'].cumsum().apply(np.exp)\n",
    "test['cstrategy'] = test['strategy'].cumsum().apply(np.exp)"
   ],
   "id": "32eea0b940cf989b",
   "outputs": [],
   "execution_count": 52
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:56:22.261917Z",
     "start_time": "2024-08-24T00:56:21.469631Z"
    }
   },
   "cell_type": "code",
   "source": [
    "test[['creturns', 'cstrategy']].plot()\n",
    "plt.show()"
   ],
   "id": "56f6d109cfc2a8f5",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 53
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:56:26.428006Z",
     "start_time": "2024-08-24T00:56:26.420312Z"
    }
   },
   "cell_type": "code",
   "source": "ptc = 0.000059",
   "id": "7e2e6d411198ec5e",
   "outputs": [],
   "execution_count": 54
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:56:28.957291Z",
     "start_time": "2024-08-24T00:56:28.947633Z"
    }
   },
   "cell_type": "code",
   "source": [
    "test['trades'] = test.position.diff().abs()\n",
    "test.trades.value_counts()"
   ],
   "id": "e48125a3334a2d9d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trades\n",
       "0.0    38420\n",
       "1.0      508\n",
       "2.0       13\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 55
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:56:30.695159Z",
     "start_time": "2024-08-24T00:56:30.689731Z"
    }
   },
   "cell_type": "code",
   "source": "test['strategy_net'] = test.strategy - test.trades * ptc",
   "id": "9a2cc3e078978ac7",
   "outputs": [],
   "execution_count": 56
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:56:31.599928Z",
     "start_time": "2024-08-24T00:56:31.589520Z"
    }
   },
   "cell_type": "code",
   "source": "test['cstrategy_net'] = test['strategy_net'].cumsum().apply(np.exp)",
   "id": "d5ce7d90c5ca8926",
   "outputs": [],
   "execution_count": 57
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-08-24T00:56:33.980685Z",
     "start_time": "2024-08-24T00:56:32.759772Z"
    }
   },
   "cell_type": "code",
   "source": [
    "test[['creturns', 'cstrategy', 'cstrategy_net']].plot(figsize=(12, 8))\n",
    "plt.show()"
   ],
   "id": "95fd7f5f06c66ded",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 58
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "a228f1ea15c8c1be"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}